WO2002057987A2 - System and method for association of object sets - Google Patents
System and method for association of object sets Download PDFInfo
- Publication number
- WO2002057987A2 WO2002057987A2 PCT/US2002/001110 US0201110W WO02057987A2 WO 2002057987 A2 WO2002057987 A2 WO 2002057987A2 US 0201110 W US0201110 W US 0201110W WO 02057987 A2 WO02057987 A2 WO 02057987A2
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- object set
- sets
- association
- modified
- data
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
Definitions
- the present invention relates to association modeling as applied to more than one set of objects.
- Pairs of elements, taken from the two sets, can be associated element-by-element, optionally yielding "pair association values" which describe the strength or adequacy of the association.
- Two sets can also be associated in a global sense, for example after associating all of the co ⁇ esponding elements of the two sets, optionally yielding an "overall association value,” indicative of the strength or adequacy of the overall global match between the two sets.
- the overall association value may be a function of the pair . association values.
- a typical assignment problem in the art of "matching theory” considers the association between two sets of objects such that every element in one object set is matched to one and only one element from the other set.
- the objective of this problem is to find an admissible association between all objects of the two sets such that the overall association value is maximized. For example, consider the problem of assigning 10 people to 10 tasks, where the objective is to match each person to a unique task such that the overall association value is as large as possible.
- the pair association value for matching person i to task j may indicate the value person i would generate for the organization each day if assigned to task j.
- the pair association values are often known apriori. Lovasz discusses some of these concepts generally in Matching Theory, North Holland Press, 1986.
- One application that requires both matching and infe ⁇ ing quantities from data includes "one-to-one marketing” or "personalization", where businesses are required to match their customers to products either at an individual level or at a segment level to increase sales. Primarily marketing and retail operations aim to satisfy the individual needs of a customer by studying to the customer's past behavior and profile. It has also been recognized that a match or association between two data sets may be optimized.
- a na ⁇ ow field of art sometimes known as “matching theory,” has developed around determining the optimal association of data sets. If a desired outcome is known to exist, then two sets can be matched, or their elements a ⁇ anged element- wise until the optimum outcome is attained. Sometimes this involves permuting the order of two sets until the optimum association configuration is achieved. Similarly, if the optimum association is not made, then an association satisfying some requirement or criterion can be admitted.
- a related field of art is that of "personalization,” wherein primarily marketing and retail operations aim to satisfy the recognized individual needs of an individual customer by studying and catering to the customer's needs and preferences in order to increase sales to the customer. This field is sometimes known as “one-to-one marketing.”
- a customer's profile data may be constructed from monitoring the customer's past or present activities. For example, a customer who purchased a computer is assumed to be interested in and the purchase of computers in the future. These kinds of assumptions are sometimes valid, but are generally simplistic and often wrong. In our example, the customer who just bought a computer is probably not interested in buying another computer for some time and marketing solicitations, such as e-mail notices and web-based pop-up advertisements can have a negative effect on the customer's future shopping experiences.
- the customers are identified upon accessing the web site by logging in using some unique login identifying information, or by being recognized on the basis of a network address associated with their client computer, or some other identifying data stored thereon.
- Cu ⁇ ent systems and methods lack versatility and cannot be generalized beyond their narrow application fields, e.g. retail marketing.
- One particularly poor aspect of present systems at one end of the spectrum is their inflexible reliance on personal profiles associated with customers at the lowest level. That is, present systems tend to generate and maintain at least N customer profiles to service N customers at a one-to-one level, even if N is very large.
- the resulting large databases scale at least linearly in N when each customer is associated with a plurality of attributes or products.
- Large computer resources in memory, storage, and processing are expended to service the customers in this way.
- the systems which over generalize and do not provide solutions that suit particular customers using the systems.
- some embodiments of the present invention are directed to a method of determining a prefe ⁇ ed grouping scheme, comprising: (A) modifying a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set are of different cardinalities; (B) modifying a second original object set to yield a second modified object set; (C) calculating a value of a metric taken at least on the first and second modified object sets; and (D) repeating any of (A) and (B) based at least on the value of the metric.
- Other embodiments are directed to a method of determining a prefe ⁇ ed grouping scheme, comprising: (A) modifying a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set are of different cardinalities; (B) modifying a second original object set to yield a second modified object set; (C) ordering the first and second modified object sets to yield respective first and second ordered modified object sets; (D) calculating a value of a metric taken at least on the first and second ordered modified object sets; and (E) repeating any of (C) and (D) based at least on the value of the metric.
- some embodiments of the present invention are directed to a method of determining a prefe ⁇ ed grouping scheme, comprising: (A) modifying a first original object set to yield a first modified object set; (B) modifying a second original object set to yield a second modified object set; (C) calculating a value of a metric given by a non-commutative function of at least the first and second modified object sets; and (D) repeating any of (A) and (B) based at least on the value of the metric.
- Another embodiment is directed to a storage medium on which are coded instruction, which when executed on a data processing system cause the data processing system to: (A) modify a first original object set to yield a first modified object set, wherein the first original object set and the first modified object set have different cardinalities; (B) modify a second original object set to yield a second modified object set; (C) calculate a value of a metric taken at least on the first and second modified object sets; and (D) repeat any of (A) and (B) based at least on the value of the metric.
- Still another embodiment is directed to a method of associating objects, comprising: (A) associating at least one element of a first original object set with at least one element of a second original object set; (B) modifying elements of the first original object set, producing thereby a first modified object set; (C) modifying elements of the second original object set, producing thereby a second modified object set; and (D) associating at least one element of the first modified object set with at least one element of the second modified object set.
- Some embodiments are directed to a method of associating objects, comprising: (A) segmenting a first object set into a first plurality of object subsets; (B) segmenting a second object set into a second plurality of object subsets; (C) associating at least one element of the first plurality of object subsets with at least one element of the second plurality of object subsets using an association operation; and (D) checking whether the association operation is consistent.
- An embodiment is also directed to a method of associating live data, collected by a data processing system, the method comprising: (A) sequentially receiving the live data in discrete packets; (B) placing the live data from (A) into at least one dynamic data set;
- Yet another embodiment is directed to a method for approximating a tendency distribution co ⁇ esponding to raw data from a plurality of object sets, comprising: (A) preconditioning the raw data into a form suitable for association; (B) segmenting the raw data into at least two fine-level subsets; (C) performing a first association operation between the at least two fine-level subsets; (D) aggregating the fine-level subsets to coarse-level subsets co ⁇ esponding to the fine-level subsets; (E) perfo ⁇ ning a second association operation between the at least two coarse-level subsets; and (F) comparing results from the first association operation and the second association operations.
- Still another embodiment is directed to an information filtering system comprising: a profile subsystem for defining a space of profile data with a particular taxonomy, and for identifying users into a particular partition or category of this predefined taxonomy; a manipulatable collaboration subsystem, based on feedback of site usage and a popular decision rule, for identifying a particular suite of content data and delivery scheme to associate with each partition in the profile taxonomy; a content delivery subsystem for delivering particular content in combinations, sequences, and schemes as decided by the collaboration subsystem; and a visualization and analysis subsystem for engaging projections of the collaboration subsystem by either profile- based category or content-based scheme, including category and content indicators indicating other profiles or content that is similar to the object of analysis.
- Another embodiment of the present invention addresses a method for conducting electronic commerce, comprising: (A) segmenting a customer base into a plurality of customer segments based on a set of customer attributes; (B) segmenting a product base into a plurality of product segments based on a set of product attributes; (C) matching a customer segment and a product segment based on a plurality of commercial activity events; (D) creating a matrix of customer segments and product segments containing information from joint co ⁇ elation operations; and (E) providing the information in the matrix in a manner usable for making marketing decisions in an electronic commerce system.
- Some embodiments are directed to a method for deriving co ⁇ espondence between two interactive data sets, comprising: (A) dividing a first data set into a plurality of first data set segments; (B) dividing a second data set into a plurality of second data set segments; (C) evaluating a joint distribution matrix to determine a relevance indicator for indicating relative relevance of the first and second data set segments to one another; (D) subdividing the first and second data set segments into finer segments and performing act (C) on the finer segments; (E) aggregating the data set segments into coarser data set representations having fewer segments if the relevance indicator indicates a lack of relevance between the first and second data set segments; and (F) exiting the process when the relevance indicator meets a preset condition.
- FIG. 1 shows a schematic illustration of a data processing system adapted for carrying out various aspects of the present invention.
- Fig. 2 shows a schematic diagram of a storage apparatus adapted for holding stored instructions and data.
- Fig. 3 shows an embodiment of two object sets having associations defined among them.
- Fig. 4 shows another embodiment of an association using a tabular representation.
- Fig. 5 shows an embodiment of an association producing pair association values (PAN) and a summation metric thereof.
- Fig. 6 shows an exemplary representation of an association matrix.
- Fig. 7 shows an exemplary representation of an association matrix with a traditionally-inadmissible association.
- Fig. 8 shows another exemplary representation of an association matrix with a traditionally-inadmissible association.
- Fig. 9 shows an illustrative example of a permutation to achieve a desired ordering.
- Fig. 10 shows another illustrative example of a permutation to achieve a desired ordering.
- Fig. 11 shows two exemplary and equally strong associations.
- Fig. 12 shows an embodiment of a system for ca ⁇ ying out modifications and associations.
- Fig. 13 depicts an exemplary process for associating object sets.
- Fig. 14 depicts another exemplary process for associating object sets.
- Fig. 15 depicts yet another exemplary process for associating object sets.
- Fig. 16 shows an embodiment of a system for ca ⁇ ying out a method according to the present invention, including data collection.
- Fig. 17 shows an embodiment of a proper association.
- Fig. 18 A, B show embodiments of staircase associations.
- association model generation technology has been developed and implemented according to the present invention which in some embodiments allows for determining a prefe ⁇ ed or optimum way to segment sets to achieve a useful association thereof.
- metrics have been developed for use with the present association modeling framework, the metrics indicative of the adequacy or strength of a given association between either entire sets or between elements thereof.
- the present invention includes in some embodiments an allowance for associating sets of various types and sizes, including sets having different sizes and a plurality of such sets defining a multi-dimensional association matrix formed by the cross-space of the sets' elements.
- Sets are not narrowly defined, and may be abstract in nature, described as comprising "objects.” These are then generally termed "object sets,” which may be of any large number of types. Since an object can be in the form of properties, conventional data, material objects, objects of manufacture, cu ⁇ ency, ideas, persons, organizations, or any manifestation of matter or knowledge or entity which may an element of a set, an object set as herein used is the most general and broadest type of set.
- object sets are that they may be comprised of elements which are themselves object sets.
- the present application discloses and claims methods for optimally segmenting an object super set into a plurality of object subsets.
- the segmentation is considered to have a granularity or "resolution” which can range from the coarsest resolution of a single-element object set to the finest resolution, where a substantially limitless division of objects in the object set can be implemented.
- Increasing the number of elements in an object set by further subdivision and/or redistribution of its elements is refe ⁇ ed to herein as "refinement.”
- refinement increases the cardinality (or number of elements) of the object set.
- other embodiments of the present invention provide for aggregation of an object set by grouping existing elements together to form an aggregated object set having fewer elements than the original object set.
- the aggregated object set thus having a lower or smaller cardinality than its co ⁇ esponding original object set.
- a “modified object set” comprises elements that co ⁇ espond to the elements of an original object set through either refinement, aggregation or both.
- one original element or object, A may be first split into two elements, Al and A2, and then Al may be further split into Alx and Aly in a refinement operation, while A2 can be combined with a different original element, B, in an aggregation operation.
- A may be first split into two elements, Al and A2
- Al may be further split into Alx and Aly in a refinement operation, while A2 can be combined with a different original element, B, in an aggregation operation.
- the present application uses the term “drilling” in some instances to signify moving to a more detailed finer resolution model or level of granularity, while using the term “aggregating” to signify moving up to a coarser level of granularity in the modeling.
- Modifications of object sets include aggregation, refinement, or ordering of the sets. Elements of a modified object set are sometimes called segments. Discussed below are some specific aspects of various types of modifications, which are generally ca ⁇ ied out using a modifier that can be implemented e.g. in a data processing system using software.
- This matrix represents a partition in the sense that given an n x 1 vector v, with elements in one-to-one co ⁇ espondence with an object set, Lv is a vector of cardinality k with elements in one- to-one co ⁇ espondence with a partition of the object set.
- the transpose of a partition matrix is also taken to be a partition matrix.
- modifications may comprise combinations of aggregation and refinement and possibly permutation.
- Other modifications can be constructed that are generated as sequences of aggregation or refinements. Referencing a finest grain set, these modifications become the product of multiple partition matrices or covers.
- Aggregation and refinement may be executed in software running on a data processing system as a single module or as separate aggregator and refiner elements or systems or computer code routines, all of which are modifiers.
- Permutations yield an ordered set even if starting from an ordered set before permutation, and are represented effectively by permutation matrices.
- Permutation operations may be implemented for example by a permuter built as a machine or as software running on a data processing system.
- a permuter is thus one type of modifier.
- the present application is in some part directed to the relationships or associations between two object sets, but this is meant to be for the purpose of explanation only, and the general concepts provided herein are meant to extend to multiple object sets. That is, by teaching or claiming a concept applied to two sets it is understood that those two sets can be merely two of a plurality of three or more sets to whom the concept applies.
- any discussion of a matrix is intended to be extensible to multi-dimensional constructs such as cubes, hyper-cubes, etc. This idea will be understood by those skilled in the art and a detailed explanation of which is not provided herein.
- a cross-space may be defined by two or more sets, and in the case of two object sets the cross-space of their elements forms a matrix space having elements formed by the pairs of elements of the individual object sets.
- the object sets can be presented in an ordering scheme or permutation of their elements that fixes the cross-space matrix. If we consider the cross-space mafrix as co ⁇ esponding to an association of one object set with the other, then the matrix is an association matrix H.
- H may be constructed from data in many different ways.
- H can be defined using corresponding categorical data.
- the resulting table can be represented as a matrix H with possibly missing entries.
- H may also be defined using co ⁇ esponding ordinal data or numerical data: ordinal or numerical data may be transformed into categorical by the application of a piecewise constant function. An H matrix may then be defined as above.
- the matrix His Populating the matrix His the subject of several aspects of the present invention, as well as analysis of the matrix H and metrics performed on the matrix to extract useful information regarding for example the association of the object sets.
- the matrix His populated with values / • that co ⁇ espond to the individual pair-level associations of the elements of the object sets corresponding to matrix location (i,j).
- the entries in the matrix H may be pair association values, described earlier, except that they may be of a generalized form or of a derivation such as those which will be given elsewhere in this application.
- the collection of values populating H may be thought of as a distribution, which can be a frequency distribution for example.
- the concept illustrated, supra may be extended to multiple object sets in mutual relation that provide a multi-dimensional cross space populated with data represented by triplets, multiplets, etc., rather than pairs.
- ⁇ may represent a distribution or a partial distribution.
- One class of problems arises when ⁇ represents a joint distribution over the object sets, or data sampled from such a distribution.
- a partial distribution is one with missing values, and important classes of problems over partial distributions arise from considering, for example, either missing data or constraints on disallowed combinations.
- Problems arising from ⁇ representing an underlying distribution admit important statistical interpretations, wherein normalization of the distribution may be important for proper interpretations.
- H may also represent a score matrix. There are cases when H may not represent a distribution but still characterizes a propensity or some other score between object sets. Again, missing values for unknown, disallowed, or unavailable combinations may be flagged appropriately.
- H may represent an a ⁇ ay of dimension larger than 2: for problems defined over n object sets, n > 2, one may think of H as an n-dimensional a ⁇ ay.
- a "generalized” match or association between n object sets can be represented by a table with n columns. Each row of the table is an element of the association, and the (i,j)th entry of the table describe which objects of object set j are contained in the ith element of the association. Whereas a proper association is one-to-one and onto and requires constraints on the entries of the table to be satisfied as indicated above, a generalized association has at least one element, and each element i contains at least one entry j with at least one object from the co ⁇ esponding object set. Thus, a generalized association is not constrained to be either one-to-one nor onto between any pair of object sets associated, although proper associations over the power sets of all relevant object sets may capture generalized associations.
- Data can be in any of a number of forms and types, and may include hybrid data types as well.
- data may be numerical, statistical, symbolic, ordinal, or categorical.
- Data may be organized and represented in a variety of ways.
- data may be analog or digital data and may be collected using empirical methods or may be generated using models or theoretical constructs or may be extracted from simulations using such models and constructs.
- Data may be represented and stored in a variety of ways according to its nature and the application at hand.
- digital data may be represented as binary digits (bits) in a suitable storage medium, such as on computer disks, tapes, volatile or nonvolatile media, etc. Data may be displayed again according to its nature and use.
- data may be represented for presentation to human users as tables, charts, graphs, sounds, colors, and can further comprise small or large groupings such as pairs, triplets, multiplets, or sets and subsets arranged in a ⁇ ays, matrices, hypermatrices, etc.
- Data, or a data table can be represented in some embodiments as a triple (I,C,N) of instances I, characteristics C, and values N, where N(ij) is a set of values that may depend on instance i from the set I, and characteristic j from the set J.
- a data set according to this representation is a set of data tables.
- Data can also be characterized in some embodiments by the types of sets N values are chosen from.
- datum (i,jN(i,j)) is called categorical if N(i,j) takes values representing an unordered set, ordinal if it takes values representing an ordered set, and numerical if it takes values representing an uncountable set.
- Object sets may comprise categorical data sets.
- categorical data set The term "categorical data set"
- CDS is used herein to refer to an object set whose elements share a common feature or attribute, which can be used to categorize the data.
- Two CDSs can be associated with one another as discussed earlier. This will be clarified by way of a simple example.
- An object set X of workers (xi, x 2 , ..., x n ) may be matched with a co ⁇ esponding object set Y of tasks (y- . , y , ..., y adjective) needing to be accomplished by the workers.
- One way is by an exhaustive trial and e ⁇ or approach, whereby every worker and task is matched in turn and the best overall result is measured.
- association model it is generally meant a systematic procedure or algorithm for achieving a good or a best association outcome.
- metrics or bases for measurement, sometimes colloquially referred to as yard-sticks, may be used to evaluate the strength or adequacy of an association or other operation. When a particular metric is used for evaluation it is normally used to derive quantitative values therefrom.
- h- j an element of the association matrix H.
- the pair association value h can take on one of many forms suited for expressing the co ⁇ esponding association.
- hy can be expressed as a number. This number may be expressed in some instances as a real number having a value lying between 0 and 1.
- a value of 0 may indicate the poorest possible match between the two elements, while a value of 1 may indicate the best possible match.
- the value of hy may be a number between -1 and +1 , where a value of -1 may indicate the poorest possible match, and the value of +1 may indicate the best possible match, with a value of 0 being neither a very poor nor a very good match.
- the numerical value given to hy may represent a percentage. Accordingly, in some embodiments, a value of 0% indicates the poorest match and a value of 100% indicates the best match. Alternatively, the value of -100% may indicate the poorest match, and a value of +100% may indicate the best match.
- the optimal assignment of tasks to a given worker is a process of detennining which task from a set of available tasks in Y is best suited for this particular worker.
- the problem may be similarly viewed as a process of determining which worker from a set of available workers in X is best suited to perform a particular task.
- the set of associations which is permitted comprises "admissible" associations.
- an object set having n elements may be represented as a (1 x n) vector or as a (n x 1) vector.
- an object set having n elements may be represented as a (1 x n) vector or as a (n x 1) vector.
- this association scheme provides a natural element-by-element or one-to-one association, whereby each element of X and 7 can be associated with a co ⁇ esponding single best-match element of the respective other object set.
- every worker x* can be assigned to a single task it performs the best, and each task y* is being performed by a single worker most suitable to do that task.
- a compromise must exist whereby the overall association between X and 7 is optimized, even if individual elements of the object sets are not all strictly associated with the best-suited co ⁇ esponding element of the other object set. It is useful in some embodiments to develop measures on the modified object sets.
- Balancing of an association Given a set of modified object sets and an association between them, it is possible to examine the relative impact of each element of the association towards the value of a cost function.
- a measure may thus be constructed, for example using a "gini index,” that characterizes the uniformity, or balancing, of impact across all elements of the association.
- Balancing of marginal distributions Given a set of modified object sets, it is possible to characterize the relative unifonnify of various marginal distributions computed from a distribution, H.
- Balancing of cardinality of segments Given a modified object set represented as an aggregation from some finest level object set, it is possible to explore the relative uniformity of the cardinality of each element of the modified object set.
- Homogeneity of segments Given a modified object set represented as an aggregation from some finest level object set, it is possible to characterize the homogeneity of each segment with respect to some measure, and then characterize the overall homogeneity of the modified object set with another measure.
- the function f indexed by H2 decomposes into an fl, characterizing the homogeneity of each segment, and £2, characterizing the overall homogeneity of the modified object set.
- the matrix H2 corresponds to the characteristic defining the sense of homogeneity of interest.
- the real and natural tendency of the consumers and products to associate in a particular way results in the tendency distribution. That is, there is a distribution which would represent the ideal, even if unknown, tendency of each consumer or subset thereof, to purchase or be associated with, certain co ⁇ esponding products on the market or subsets thereof. This tendency distribution is of great importance, as it can provide valuable marketing information to producers of goods and services.
- tendency curve or distribution One aspect of the tendency curve or distribution is that it exists whether or not it is measured or known. There is a natural propensity leading to the ideal match that would become evident given sufficient data and measurement capabilities. The fact that the tendency curve is unknown, or only known with finite certainty, does not always or significantly detract from its value. After all, consumers are normally constrained by their circumstances and the markets available to them, and will still select from the available product choices, even if better choices could be made available to them in a hypothetical ideal market. Thus, a producer of goods or services might desire to investigate the tendency distribution, or to approximate it, for the purpose of delivering products and services which better satisfy the available consumer pool.
- the vendor of goods may wish to investigate which demographic segments to target for advertising or marketing of the products. In other words, it may be more feasible or profitable to alter or tailor the pool or buyers than to alter or tailor the pool of products presented to the buyers.
- the present inventors have recognized that a feedback exists between the market and the market model. This inter-dependence of data and data model has been incorporated into the overall modeling framework of the present invention. As an illustration, both the market and its choices have an impact on the purchasing behavior of the consumers and the purchasing behavior of the consumers influences the development and shape of the marketplace. So not only does the purchase behavior of a consumer segment influence sales figures for a product segment but results of marketing studies using the sales data will then be used to generate advertising and targeting campaigns that will in turn influence the sales data, and so on.
- One aspect of the present invention is the approximation of the tendency distribution or curve by another distribution or curve. This approximation may be useful if the actual or ideal tendency distribution cannot be found or measured. Thus an efficient means for discovery or approximation of the tendency distribution is desired, and ways of achieving this approximation are provided herein according to various embodiments of the invention. Accordingly, by judicious selection of subsets of data, or objects generally, and then by associating the resulting data or object sets, an acceptable representation of a match, which may co ⁇ espond to the ideal tendency distribution, may be obtained.
- Attention is directed next to an aspect of the present invention which can provide in some embodiments a more efficient method for analyzing associations and obtaining optimizations thereof.
- the invention disclosed herein allows for a flexible multi-resolution analysis. Accordingly, it is disclosed that some embodiments call for examination of aggregated object sets or refined object sets, collectively "modified object sets.”
- This aspect of the invention permits a top-down analysis as well as a bottom-up analysis. Associations are thus performed at any level of resolution by aggregating and/or refining the original object sets. The process can be repeated iteratively.
- the object sets are iteratively modified by refining and aggregation of the elements thereof until an optimum representation of the constituent data and association thereof is obtained.
- metrics indicative of the strength or adequacy of a match between two object sets are generated at each level of resolution selected for analysis and, based on the value of the metric, it is determined whether to drill down to finer resolution in all or some elements of an object set and repeat the metric measurement, or whether to aggregate more than one element into a single aggregated element in the modified object set.
- the method and system of the present invention return a model or solution to the user.
- the present invention involves associations at multiple resolution, and incorporates this concept into many embodiments thereof. Solving a problem, as described elsewhere in this document, over n object sets sometimes involves constructing a set of modifications that characterize a prefe ⁇ ed collection of modified sets and a prefe ⁇ ed association between them.
- the nxl cardinality vector with entries given by the cardinality of a co ⁇ esponding modified object set, characterizes the resolution of the problem and it's resulting solution.
- the nesting property can comprise one aspect or characteristic of multiresolution associations.
- Nesting can define a topology over the object sets. For example, consider an original object set that is partitioned into an original objects set 1, OS 1, and an original object set 2, OS2, each of cardinality n. Suppose an optimal association is found for every resolution k, k ⁇ n, and it is discovered that they are nested. Then a tree may be constructed over the element of OS1 defining which elements were aggregated at each resolution (likewise for OS2). These trees induce a topology on these sets. Moreover, various metrics between elements may be defined from these topologies, possibly also considering the value of the cost function at the corresponding resolution or other properties of the association.
- the segments are mutually exclusive (ME) if and only if ⁇ ,- ly ⁇ 1 3. the segments are collectively exhaustive (CE) if and only if j l ⁇ 1
- Nonlinear cost functions may be considered and may be composed using the above aggregation function q.
- L k HR k by a weight. This can be represented by an element- wise multiplication of a full matrix Wto yield L k HR k oW.
- This is referred to herein as the "weighted trace formulation" trace (L k HR k o W) Case 3.
- the general association problem at a resolution k can be defined as follows: max / (L k ,R k ,Hl)
- Lk>Rk min ; - (L k H R k ) u , L k and R k are standard aggregations.
- Lk,Rk cost function constitutes a generalization of the trace function.
- c can represent summing both diagonal and upper diagonal elements of the argument.
- Another way of addressing avoidance constraints is to use assign the value -1 to the (i,j) terms that are not to be aggregated. We then follow the standard formulation which will prevent aggregations and matching of undesirable segments.
- H represents partial information of a full distribution
- the missing elements simply indicate where values of the distribution are uncertain.
- aggregations over missing elements are allowed, but the quality of a match between aggregations containing uncertain elements may be dubious.
- a lowerbound to the expected performance of a match can be obtained by considering the missing values as variables
- v(k) max trace (L k YR k ).
- assignability function v is a lower bound to the usual assignability function ⁇ that could be achieved without uncertainty.
- LR is applicable when/'s in the formulation section are linear in L k with fixed Rk and linear in R k with fixed L k .
- L G L k egA where gA is the set of all L.
- the problem ma LkegA trace(L k HG L ) is a linear program.
- the search over L k is now replaced by a search over a vector g.
- This approach converted the nonlinear problem by a search over linear problems.
- Various aspects of the present invention allow for an automated, e.g. computerized, control of the drilling based on rales. This allows for inclusion of machine learning models to further enhance the invention by using expert systems or others to facilitate efficient determination of prefe ⁇ ed segmentations and representations of modified object sets that yield useful approximations of tendency curves and other benefits.
- a human "pilot" or operator can be used to direct the process.
- the pilot may be someone skilled in the art of association or marketing or another art relevant to the application at hand.
- the pilot may use his or her skills and prior experience for example to decide where to start the drilling and analysis process or to decide on initial compositions of the associated object sets.
- the pilot can determine also when a sufficient criterion for optimum association has been reached and decide to end the process and report the results.
- Top-down typically implies that a cardinality of the feature set describing all object sets remains constant or increases with each iteration of the process. According to some aspects of the top-down process, computational complexity is reduced and it allows for the development of parsimonious models with small amounts of data.
- the process may be broken down into 3 general phases: data collection, model building, and association implementation and tracking. Note that some of the sets within each phase of the process are optional and may depend on the application.
- data is collected and formatted such that at least 2 distinct object sets are well defined and represented by a set of features.
- the formatting process may consist of extracting data from legacy systems or data warehouses into stractured tables that easily render the construction of the data matrix H.
- a human pilot specifies her objectives in executing the associations, and any consfraints she may have on the associations. For example, the pilot may wish to restrict the number of associations to be generated due to high cost of executing each association. Or, the pilot may wish to restrict associating particular objects with other objects.
- the automated part of the process then constructs the data matrix H, and computes a multi-resolution association model, which comprises the optimal modified sets, their representations, and their associations for resolutions 1 to the smallest cardinality of the original object sets.
- the system selects one resolution model based on some criteria, which may include pilot-induced constraints, or previously defined notions of consistency, balance, and homogeneity, or other measures on the modified object sets or associations.
- This selected model is herein referred to as the "single association model (SAM)".
- the representations of its modified object sets are simplified to generate what is herein refe ⁇ ed to as their "minimal descriptions".
- the system selects a set of "relevant" features (a subset of the feature set that represents the two original object sets) that best describe the modified objects within the SAM.
- a feature of the original set is determined to belong to the relevant set based on a criterion that measures how necessary the representations of the modified sets require that particular feature.
- the new representations of the modified object sets spanned by the set of relevant features may be equivalent to or an approximation to their original representations.
- the structure of the SAM also renders suggestions on where and how to drill within the SAM. Determining where to drill requires determining which segments lacks homogeneity. It is desirable to drill within an element of the modified object set, a segment, that is not very homogeneous. Determining how to drill within a non- homogeneous segment requires analysis on pair association values involving that segment, that are significant but not in its association. These drilling suggestions are interpreted by the pilot, who determines what new features to use to improve the SAM. The pilot or the system constructs focus and control groups within each element of the modified object sets, for which she will execute the association as determined by the SAM.
- each element of a modified set consists of one or more element from the original object set (or one or more "finest grain element").
- the focus and control groups created for each element of the modified object sets thus each comprise a representative subset of the finest grain elements.
- the results are then tracked by the system and used to determine how to modify the size of the focus groups. For example, executed associations on focus groups that contribute positively towards the pilot's objectives will be positively reinforced, whereas executed associations that contribute negatively will be de-emphasized.
- one aspect of evolving the focus groups as described above is to start off with each having the same cardinality. Then, when a focus group is determined to contribute positively, the system or pilot increases the cardinality of that group. When a focus group is determined to contribute negatively, the system or pilot decreases the cardinality of that group. While associations are being executed, the new features injected by the pilot may be incorporated and the process above may iterate to improve the pilot's objectives.
- pilot is not limited to only the human pilot described, supra, but may also be a machine that substantially acts in a way similar to that described for the human pilot.
- Expert systems or intelligent agents may thus be used as pilots in the present invention and the term "pilot" is intended to encompass any such agents or machines.
- SAMs can be used to describe and enable the exemplary applications. Both classes take into account a business 's data set and partitions the set into two, one being a customer data set and the other being a product data set. The partition of the data set is thus in some but not necessarily all cases restricted to be bipartite.
- the modified object sets resulting in the SAMs are: the set of customer segments and the set of product segments. Note that elements of a product segment may be either individual products or distinct bundles of products.
- One feature of the two classes of SAMs is the cost function over which they are optimized. The two cost functions over which the SAMs are aligned with the associations comprise a proper association and a staircase association.
- the cost function is the sum of the staircase elements of LHR, as shown in Figure 18.
- the associations consist of the staircase elements of LHR.
- “Targeting” or “personalization” entails customization to a customer's or customer segment's needs and preferences to increase customer satisfaction and profit. For example, if a customer is shopping on-line using a vendor's web site, she experiences personalization when the vendor's web site only shows her content of shoes that she (or the segment in which she belongs) would be interested in instead of showing her all types of shoes.
- An equivalent off-line example would be that a customer walks into the vendor's store and the store is automatically a ⁇ anged in a manner such that everything she (or her segment) is interested in purchasing is in the front of the store or highly visible to her, while the rest of the shoes are either not displayed or less visible to her.
- Targeting can be sometimes considered a by-product of the proper SAM constructed as described above.
- the targeting rules are then simply the associations of the SAM since each element of one modified set is a customer segment and each element of the other modified set is a product group. Note that the size of a customer segment may have cardinality equal to 1 if the original object sets elements consist of individual customers. Thus, targeting is addressed by the proper SAM at both the individual and segment levels.
- Policies or promotions are typically short-term offers that consist of one product or an assembly of products packaged and priced to increase profit.
- the designs of various types of promotions are facilitated by SAMs. These promotion types include individual-level, segment-level, isolated opportunistic, segment-collaborative, and segment arbitrage promotions.
- Standard global policies typically involve pure discounts on the same set of products and are executed on all customers, whereas individual-level and segment-level promotions typically involve pure discounts on different products targeted to different individual customers or different groups of customers, respectively.
- Such policies may be designed from the proper SAM.
- the targeted segments are defined by the modified customer set of the SAM, and the discounts targeted to each segment are applicable to a subset of products in its associated product segment.
- Isolated opportunistic promotions are designed to be attractive primarily to a particular segment for which the business is trying to achieve a specific change in behavior, such as lifting frequency of purchase or lifting volume per transaction.
- Such policies may be designed from the proper SAM constructed as described above.
- the targeted segments are defined by the modified customer set of the SAM, and the products that they are associated with tend to be sets of products they buy. Thus, if one observes that the purchase frequency, for example, of a segment is smaller than the average across all customers, then one may design a promotion that offers a discount coupon that may be used only on the next visit (which expires after some date) on one or more of the products in the product segment they are associated with. This gives customers in the targeted segment an incentive to return to the store soon, which may potentially lift its overall purchase frequency.
- Segment collaborative policies attempt to promote a set of products to a segment that has shown no prior history of buying that set, but shares common needs with another segment that does purchase the promoted set of products. Common needs of these two segments are infe ⁇ ed from the existence of overlapping purchases in other products.
- Such policies may be designed from the staircase SAM constructed as described above.
- One example is to consider the case of figure 18 A.
- Each customer segment block now shows a sub-group of customers that buy products from two product segments, and the rest of the block segment buying products from just one of the two product segments. For example, as shown in Figure 18 A, Cl tends to buy products from PI and P2 while C2 tends to buy only P2.
- Cl tends to buy products from PI and P2 while C2 tends to buy only P2.
- Segment arbitrage policies are structured to increase a behavioral lever with respect to a product for which the targeted segment seems to have an affinity, using knowledge that there exist other segments that show that the desired behavior is possible.
- association values would imply that there may be opportunity to increase the value of associating customer segment 1 to product segment 2, since customer segment 2 has a high value to be associated with product segment 2.
- sensitivity analysis may be performed to understand the market better.
- Such analysis includes measuring elasticities to price, product types, and purchase behavior such as frequency and volume.
- arbitrage promotions may be executed to segments to determine how much segments "like" or “need” certain products. For example, if customer segment 1 of figure 18A received the arbitrage promotion described in the earlier example, and these customers did not respond well to the promotion, then this may indicate that regardless of how the business prices products in product segment 2, customer segment 1 simply won't purchase more from product segment 2.
- staircase SAM Another functionality enabled by at least some embodiments of the staircase SAM is the design of new product bundles. Both the segment arbitrage and segment collaborative promotions use the staircase SAM to design and test out new bundles (cross-sells) to customer segments.
- SAM design of new product hierarchies.
- the SAM generates product segments, which may in turn be directly translated into a novel way to re-organize a business's product classification scheme.
- This may not be the optimal way to group products if it is difficult to distinguish between a motorcycle buying customer and a car buying customer.
- the proper SAM groups the products into sporty vehicles and family vehicles because two distinct customer segment purchase one or the other exclusively, then it may be to the business's advantage to rethink how they think about their product classification scheme.
- Some aspects of the invention return visual data such as frequency distribution diagrams or histograms which can be analyzed according to those methods for analyzing visual data known to those skilled in the art. For example, a skilled pilot or a machine having been programmed with pattern recognition routines may be able to determine the adequacy of an emerging association by analyzing the distribution contour or slices taken therethrough. Other representations such as scatter plots and graphs can be used to evaluate the results of a drilling process according to aspects of the present invention.
- Yet another aspect of the present invention relates to achieving "consistency" in the context of association modeling and object set design.
- the iterative process of achieving the optimum association of object sets is evaluated for convergence.
- convergence it is meant that a diminishing improvement is generally realized and that that diminishing improvement is co ⁇ elated to an incremental approach of some quantity or entity towards its ideal or optimum state. It should be understood that local variations might occur, but that in most cases the process can converge to an optimum monotonically or substantially so. It should also be understood that depending on the starting point of the iterations, e.g. by selecting a particular object set initial resolution and composition and permutation, local optima might exist.
- the present invention is capable of utilizing prior known techniques such as linear programming for deriving optimizations in the process, but is not so limited and is intended to encompass the presently-discussed optimizations as well as others not specifically disclosed but known to those skilled in the art and of equivalent nature and utility.
- consistency is equivalent to the convergence of an estimated quantify to some stationary distribution.
- E ⁇ or estimates using asymptotic theory, laws of large numbers, and "Chi-squared" distributions may be used for this purpose. Care needs to be taken to identify precisely the quantity that is consistent.
- a sample path distribution that approaches a periodic distribution is clearly consistent. This follows from the fact that periodic maps are stationary on a lifted domain.
- a sample path distribution that does not normally converge may converge if distribution is aggregated over some fixed interval length. This is equivalent to a weaker notion of convergence, refe ⁇ ed to as "weak convergence". Weak convergence includes the case where samples of a sequence converge. It should be noted that if one is interested in observing how a sequence of optimization problems is consistent, one may look at the set of all optimal solutions in the context of convergence.
- Examples of such convergence include convergence of trace(LH(t)R), or convergence of the match values (LH(t)R).
- Cross validation as a way to quantify the degree of consistency of a model.
- Cross validation can be broken down into the following steps: (1) computing a model, and obtaining L,R; (2) validating with a new set of data using or having an allowable e ⁇ or criterion. In some cases this means populating the new data set using the same model and measuring an e ⁇ or between the past and new distributions at the aggregate level; (3) repeating the match to see if it is stable or has converged. Problems posed over finite domains admit solutions by exhaustive search, although the computational complexity of such solutions may be impractical in some situations.
- nesting routines can be implemented, where the nesting requirement is relaxed at coarser resolutions when the problem complexity reduces, thus freeing up computational resources to relax simplifying assumptions on the solution. Moreover, randomization procedures can be used to yield good solutions with high probability.
- H comprises data sampled from a relevant distribution.
- the modeling aspect of the problem arises from considering how to characterize the matching properties of an underlying distribution from which the data is sampled.
- H(t) is comprised of samples from a distribution, where t indexes the sampling
- the formulations mentioned elsewhere become dynamic with potentially dynamic solutions.
- Two cases of such sampling is when the data is collected "real time,” meaning on the order of the cycle time from policy execution to response measurement, or when H(t) is warehoused for subsequent time series analysis.
- various internet applications have considered “live” data processing where the cycle time for real-time response is short and even next-day processing is considered “batch". In either case, however, convergence properties of these solutions as the number of samples increases become important in the analysis of the predictive quality of any model derived from such solutions.
- assignment modeling and consistency validation do not require time series data. Since the resulting assignment model is static, other, wealcer notions of consistency apply. Here we explore, in the context of other, standard notions of consistency, novel forms of consistency that are meaningful in particular for association models.
- aspects of the present invention are ca ⁇ ied out on a data processing system or on a computer system.
- a computer system 1300 is shown in Fig. 1.
- Various elements of the embodiments described herein, either individually or in combination, may be implemented on the computer system 1300.
- the computer system 1300 includes at least one main unit coupled, directly or indirectly, to one or more output devices 1301 which transmit infonnation or display information to one or more users or machines.
- the computer system 1300 is also coupled, directly or indirectly, to one or more input devices 1302 which receive input from one or more users or machines.
- the main unit may include one or more processors 1303 coupled, directly or indirectly, to a memory system 1304 via one or more interconnection mechanisms 1305, examples of which include a bus or a switch.
- the input devices 1302 and the output devices 1301 are also coupled to the processor 1303 and to the memory system 1304 via the interconnection mechanism 1305.
- the computer system 1300 may further comprise a storage system 1306 in which information is held on or in a non- volatile medium. The medium may be fixed in the system or may be removable.
- the computer system 1300 may be a general purpose computer system which is programmable using a computer programming language.
- Computer programming languages suitable for implementing such a system include procedural programming languages, object-oriented programming languages, macro languages, or combinations thereof.
- the computer system 1300 may also be specially-programmed, special-purpose hardware, or an application specific integrated circuit (ASIC).
- ASIC application specific integrated circuit
- the processor 1303 is typically a commercially-available processor which executes a program called an operating system which controls the execution of other computer programs and provides scheduling, input/output and other device control, accounting, compilation, storage assignment, data management, memory management, communication and data flow control and other services.
- the processor and operating system define the computer platform for which application programs in other computer programming languages are written. The invention is not limited to any particular processor, operating system or programming language.
- the medium 1401 may, for example, be a disk or flash memory.
- the processor 1303 causes data to be read from the nonvolatile recording medium 1401 into another memory 1402 that allows for faster access to the information by the processor 1303 than does the medium 1401.
- This memory 1402 is typically a volatile, random access memory (RAM), such as a dynamic random access memory (DRAM) or static random access memory (SRAM). It may be located in storage system 1306, as shown in Fig. 2, or in memory system 1304, as shown in Fig. 1.
- the processor 1303 generally manipulates the data within the integrated circuit memory 1304, 1402 and then copies the data to the medium 1401 after processing is completed.
- a variety of mechanisms are known for managing data movement between the medium 1401 and the integrated circuit memory element 1304, 1402, and the invention is not limited thereto.
- the invention is also not limited to a particular memory system 1304 or storage system 1306.
- aspects of embodiments of the invention may be implemented in software, hardware, firmware, or combinations thereof.
- the various elements of an embodiment, either individually or in combination, may be implemented as a computer program product including a computer-readable medium on which instructions are stored for access and execution by a processor. When executed by the computer, the instructions instruct the computer to perform the various steps of the process.
- Figure 3 shows an illustrative representation of two object sets 100 A and 100B, the object sets containing various objects 110A and HOB for example. As described previously, associations may be performed between the object sets 100 or the objects contained therein 110. An association between two objects, e.g. 110A and 110B, is depicted in the figure by A-connector 120.
- FIG. 4 Another way to consider associations is shown schematically in Figure 4.
- the figure shows a table 130 comprising elements of two object sets 100 A and 100B.
- the associations between two objects e.g. 110A and 110B, are given by placing the objects 110A and 110B in co ⁇ esponding positions of the table 130.
- the table 130 shown in Figure 4 only provides the associations formed between the object sets 100A and 100B, without providing any quantitative indication of the strength of the association between the two object sets or the constituent objects therein.
- Figure 5 shows, also by way of a table, not only the associations made between the two object sets 100 A and 100B, but also provides the pair association values co ⁇ esponding to each pair of objects from the two object sets which have been associated with one another.
- Figure 5 shows an association being made between two objects, Cl and P2, which yields a pair association value of 4.
- Pair association values 130 are given for each of the associations made between the various pairs of elements in sets OS1 and OS2.
- Figure 5 also goes on to show a metric indicative of an overall association value between the two object sets OS 1 and OS2. In this example, a sum is taken over all pair association values 130 created from associating the individual elements of the object sets OS1 and OS2.
- a plurality of matrix elements 142 are then able to be populated with a distribution comprising data which co ⁇ esponds to the pair association values formed by the objects of the object sets co ⁇ esponding to the position in the matrix of a given mafrix element 142.
- This is of course not meant by way of limitation, and any other metric may be used under suitable circumstances to populate the matrix 140.
- Figure 6 shows some elements of the matrix highlighted in bold type and boxed, e.g. 144.
- This is meant to depict illustratively a one-to-one assignment which assigns each particular object from the first object set 100A to a co ⁇ esponding particular object from the second object set 100B.
- the object sets 100 A and 100B co ⁇ espond to products (P1...P4) and customers (C1...C4) respectively, then the second product P2 has in this example then associated with an assigned to customer segment or object Cl .
- Matching an assignment criteria can take on many forms as has been described, but in some embodiments the assignment is based at least on a pair association value is used for this purpose.
- One may then use any of various techniques, e.g. exhaustive searching or linear programming techniques to contemplate which assignments would yield an optimum or a prefe ⁇ ed arrangement of the elements of the object sets 100A and 100B.
- FIGS. 7 and 8 show two examples of traditionally-inadmissible assigmnents according to some embodiments of the present invention.
- a single object 118A from a first object set 100A appears to be assigned to two different objects 118B and 118C from the second object set 100B.
- Figure 8 shows an inadmissible assignment assigning two different objects 118D and 118E from the first object set 100A with a single object 118F taken from the second object set 100B.
- a ⁇ ange particular associations or assignments of objects along a diagonal of a matrix defined by the cross-space formed by two object sets 100A and 100B.
- the permutation which may be implemented as an operator or a matrix multiplication operation, yields a permuted matrix 142 co ⁇ esponding to a permuted object set 102B.
- the assignments appear along the diagonal of permuted matrix 142.
- a metric for example the trace of matrix 142 may then be computed in an exemplary embodiment to calculate a value of the trace metric which can be indicative of an overall association value between the object sets 100 A and 100B.
- the columns corresponding to elements of the first object set 100 A may alternatively be permuted. This is shown in Figure 10, where elements of the first object set 100A are permuted to form an object set 104A such that the entries co ⁇ esponding to assignments between the elements of the object sets lie along a diagonal of the matrix 144.
- a value of a metric such as the trace of matrix 144, may be computed easily when the permuted matrix 144 is obtained.
- both the columns and the rows of a matrix 140 may be permuted to obtain a desired a ⁇ angement of the elements of the matrix 140. It should also be noted that similar analogous techniques may be implemented on matrices having greater than two dimensions, such as data cubes and hypercubes, etc.
- Figure 12 shows schematically an overview of how a value of a metric is derived from associations and operations and modifications of two object sets according to some embodiments of the invention.
- object sets 100 A and 100B are modified using two modifiers, 150A and 150B, operating respectively on the object sets 100A and 100B.
- the object sets 100A and 100B can be considered in some cases "original object sets" and may be populated in some cases with original or raw data obtained from some data collection process or mechanism.
- the modifiers 150 A and 150B may comprise means for aggregating, refining, permuting, or otherwise expanding or reducing or reordering of the two original object sets.
- the modifiers 150A perform a modification on the original object sets and yield two co ⁇ esponding modified object sets 160A and 160B.
- the modified object sets 160A and 160B may then be associated by an associator 170 as described elsewhere in this document.
- the associator may perform any function in general or operation on at least an element of sets 160A and 160B to yield a value of a metric being used by the associator 170.
- the associator 170 may perform an association on elements of the modified object sets 160A and 160B to yield at least of one pair association value, or the associator 170 may perform an overall association operation on the entire modified object sets 160A and 160B.
- the associator produces an output value
- Figure 13 shows schematically a plurality of acts, which may be ca ⁇ ied out in any order suitable for the application, according to various embodiments of the present invention.
- data is collected, which may be live data, for populating a distribution matrix or a database.
- Objects or categories or segments may be formed thereon or extracted therefrom to create first and second object sets, e.g. customers and products.
- Acts 1000 and 1010 respectively comprise modifying a first and second original data set to yield first and second modified object sets.
- act 1020 a metric is used and a value of the metric is calculated at least on the first and second modified object sets.
- Acts 1000 and/or 1010 are repeated as necessary, optionally in a loop which can further comprise performing act 1020 and others. This repetition is depicted in act 1030 of the figure.
- auxiliary acts may be performed, some of which include performing an association (1040), satisfying a stopping criterion (1050), determining whether consistency has been achieved (1060), or other acts.
- a stopping act is drawn to indicate exiting a loop or a repetitive process, perhaps upon achieving convergence or consistency or based on some criterion for stopping.
- Figure 14 shows schematically a process comprising modifying a first and second object set to yield a respective first and second modified object set in acts 2000 and
- first and second modified object sets are ordered to yield corresponding first and second ordered modified object sets in act 2020.
- a value of a metric is calculated as before in act 2030, the value being a function of at least the first and second ordered modified object sets.
- This group of acts comprising acts 2020 and 2030 can be repeated according to the act described as 2040.
- FIG. 15 illustrates schematically an embodiment of a method according to the present invention that includes solving a linear program for optimization.
- act 3000 choosing a first initial permutation co ⁇ esponding to an ordering of a first object set is performed.
- Act 3010 is directed to solving a first linear program for a second permutaion while the first pennutation is held fixed.
- Act 3020 comprises solving a second linear program for the first permutation while keeping the second permutation fixed.
- acts 3010 and 3020 are repeated until any of the first and second linear programs satisfies a criterion or converges, after which the process may stop.
- Figure 16 depicts an illustrative of an embodiment of a system 500 according to the present invention.
- Data may be obtained either from a storage site or database 700 or from a live source such as a network 600.
- the data is put into object sets such as by using a data processor shown in Figure 1 earlier.
- the object sets are acted on using the modifier 150, which couples the data sources 600, 700 to a calculator 800 that calculates a metric taken on at least the object sets.
- an associator 170 may be used, such as an aggregator, refiner or permuter to perform an association of the object sets or information related thereto.
- the system 500 includes a means 190 for determining whether a function of the value of the metric is consistent.
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
Claims
Applications Claiming Priority (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US26220001P | 2001-01-16 | 2001-01-16 | |
US60/262,200 | 2001-01-16 | ||
US10/051,548 | 2002-01-16 | ||
US10/051,548 US20020161561A1 (en) | 2001-01-16 | 2002-01-16 | System and method for association of object sets |
Publications (2)
Publication Number | Publication Date |
---|---|
WO2002057987A2 true WO2002057987A2 (en) | 2002-07-25 |
WO2002057987A8 WO2002057987A8 (en) | 2002-09-19 |
Family
ID=26729537
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/US2002/001110 WO2002057987A2 (en) | 2001-01-16 | 2002-01-16 | System and method for association of object sets |
Country Status (1)
Country | Link |
---|---|
WO (1) | WO2002057987A2 (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112988815A (en) * | 2021-03-16 | 2021-06-18 | 重庆工商大学 | Method and system for online anomaly detection of large-scale high-dimensional high-speed stream data |
-
2002
- 2002-01-16 WO PCT/US2002/001110 patent/WO2002057987A2/en not_active Application Discontinuation
Non-Patent Citations (1)
Title |
---|
No Search * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112988815A (en) * | 2021-03-16 | 2021-06-18 | 重庆工商大学 | Method and system for online anomaly detection of large-scale high-dimensional high-speed stream data |
CN112988815B (en) * | 2021-03-16 | 2023-09-05 | 重庆工商大学 | Method and system for online anomaly detection of large-scale high-dimensional high-speed stream data |
Also Published As
Publication number | Publication date |
---|---|
WO2002057987A8 (en) | 2002-09-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Brynjolfsson et al. | The great equalizer? Consumer choice behavior at Internet shopbots | |
US10719521B2 (en) | Evaluating models that rely on aggregate historical data | |
Bandyopadhyay et al. | Product recommendation for e-commerce business by applying principal component analysis (PCA) and K-means clustering: benefit for the society | |
Huang et al. | A case study of applying data mining techniques in an outfitter’s customer value analysis | |
CN110956273A (en) | Credit scoring method and system integrating multiple machine learning models | |
US20040138958A1 (en) | Sales prediction using client value represented by three index axes as criteron | |
Schmalensee et al. | Perceptual maps and the optimal location of new products: An integrative essay | |
Yang et al. | Big data market optimization pricing model based on data quality | |
CN113157752B (en) | Scientific and technological resource recommendation method and system based on user portrait and situation | |
US20020161561A1 (en) | System and method for association of object sets | |
CN114219169A (en) | Script banner supply chain sales and inventory prediction algorithm model and application system | |
WO2010019897A1 (en) | Automatically prescribing total budget for marketing and sales resources and allocation across spending categories | |
Hemalatha | Market basket analysis–a data mining application in Indian retailing | |
CN114077980A (en) | Intelligent supplier management system and intelligent supplier management method | |
Satish et al. | Study and Evaluation of user’s behavior in e-commerce Using Data Mining | |
Even et al. | Economics-driven data management: An application to the design of tabular data sets | |
Dhurkari | Strategic pricing decision using the analytic hierarchy process | |
CN114399367A (en) | Insurance product recommendation method, device, equipment and storage medium | |
Chou et al. | The RFM Model Analysis for VIP Customer: A case study of golf clothing brand | |
CN112991026A (en) | Commodity recommendation method, system, equipment and computer readable storage medium | |
Asmat et al. | Data mining framework for the identification of profitable customer based on recency, frequency, monetary (RFM) | |
Ray et al. | Integrated approach of fuzzy multi-attribute decision making and data mining for customer segmentation | |
Sepenu et al. | A machine learning approach to revenue generation within the professional hair care industry | |
Granov | Customer loyalty, return and churn prediction through machine learning methods: for a Swedish fashion and e-commerce company | |
WO2021192232A1 (en) | Article recommendation system, article recommendation device, article recommendation method, and recording medium storing article recommendation program |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AK | Designated states |
Kind code of ref document: A2 Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ OM PH PL PT RO RU SD SE SG SI SK SL TJ TM TN TR TT TZ UA UG US UZ VN YU ZA ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: A2 Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG |
|
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
AK | Designated states |
Kind code of ref document: C1 Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ OM PH PL PT RO RU SD SE SG SI SK SL TJ TM TN TR TT TZ UA UG US UZ VN YU ZA ZW |
|
AL | Designated countries for regional patents |
Kind code of ref document: C1 Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZM ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG |
|
D17 | Declaration under article 17(2)a | ||
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
REG | Reference to national code |
Ref country code: DE Ref legal event code: 8642 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: COMMUNICATION PURSUANT TO RULE 69 EPC (EPOFORM 1205A OF 081203) |
|
122 | Ep: pct application non-entry in european phase | ||
NENP | Non-entry into the national phase in: |
Ref country code: JP |
|
WWW | Wipo information: withdrawn in national office |
Country of ref document: JP |