Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
  • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
  • G06F16/35—Clustering; Classification
  • G06F16/355—Class or cluster creation or modification

Definitions

• the invention relates to a method of unsupervised classification linear and stable on the order of objects.
• clustering It relates more generally to automatic classification techniques known as "clustering".
• Text Mining for the discovery, without a priori, knowledge (or “knowledge discovery”) in large databases.
• These data can be of structured type when it comes to behavioral or demographic data for example, or of unstructured type when it comes to textual data.
• the automatic classification consists in structuring these objects in the form of very homogeneous classes (or groups). This homogeneity reflects the fact that two objects of the same class must be more similar (or similar) to each other than two objects belonging to two distinct classes. The constitution of these classes will make it easy to detect groups of objects with similar profiles or themes depending on whether they are structured or unstructured data.
• the “mobile centers” and hierarchical classification methods arbitrarily set a number of classes.
• “first leader” methods they require the setting of a similarity threshold and are dependent on the order in which the objects are taken into account. Indeed, they can lead to completely different results depending on the order in which the objects are stored. Nevertheless, they allow to process, in reasonable times, large amounts of data. However, to obtain these performances, these methods require the fixing of a maximum number of classes very small compared to the number of objects.
• the invention relates to a linear unsupervised classification method for structuring a database of objects and descriptors, which is stable on the order of the objects comprising a preliminary step of transforming qualitative, quantitative or textual data into data.
• the method may comprise a step where the classes of the partition are grouped together.
• the merging step c) comprises, for example, a step in which the best operation (in the sense of the optimization of the criterion) of the following 4 is determined from two classes in intersection:
• the grouping of the classes of a partition is, for example, carried out on several hierarchical levels and comprises the following steps: • reduce the value of the structural threshold at each level so that weakly negative contributions can become positive,
• the invention also relates to a linear unsupervised classification device for structuring a database composed of objects and descriptors, which is stable on the order of the objects comprising a preliminary step of transforming qualitative, quantitative or textual data into binary presence-absence data characterized in that it comprises at least the following elements: a computer comprising a memory, a database and a processor adapted to implement the steps of the method having one of the characteristics previously stated.
• FIG. 1 a diagram corresponding to a generic system of a system supporting the method according to the invention
• FIG. 2 a block diagram describing the general operation of the processing chain of the field of data mining applied to a body of documents or to a digital database
• FIG. 3 a block diagram which shows the steps of the preprocessing phase which is carried out before the automatic classification process
• FIG. 4 a block diagram which represents the scheduling of the steps constituting the method of this invention
• FIG. 5 a block diagram which details the various operations performed during the processing 41 of FIG. 4,
• FIG. 7 the quantities evaluated during the basic operation described in FIG.
• Figure 8 a block diagram that shows the steps of the class hierarchical aggregation process.
• FIG. 1 illustrates, without limitation, an example of application of the method according to the invention, making it possible to automatically generate various actions to be performed from the classification result.
• the system on which the process operates comprises, for example, a computer 1 comprising a memory 2, a processor 3 associated with the classification process 5.
• the computer 1 is linked to a database 4.
• the classification result is presented for example, in the form of a set of classes stored in a suitable device 6.
• the device 6 is, for example, in connection with a server 7 sending e-mail for example.
• the server is provided with appropriate processing means known to those skilled in the art for processing the received class information and triggering mechanisms for sending messages to the persons of the classes concerned.
• Any device for driving and / or driving actions according to the results of the classification may be used.
• the description which follows is given for illustrative purposes in a general context of instructions executable in a computer program, such as program modules executed by a computer or any other computer.
• the invention can be implemented on any type of computer, electronic organizer, etc.
• the starting point can be either any database (set of individuals described by a set of numeric variables), or any body of documents. These two types of data are represented by the rounded blocks 20 and 25, respectively.
• pre-processing phase 21 which consists of conventional statistical processing such as centering or data reduction or else transformations ... etc.
• pre-treatments lead to the data table 22. It is this table that constitutes the source of the information analysis treatments.
• a body of documents during a pre-processing phase 26 each text is transformed into a vector whose dimensions correspond to descriptors obtained by a linguistic process which can be morpho-syntactic analysis, extraction of concepts, extraction of co-occurrences ..., linguistic and semantic processing etc. .
• a presence-absence (binary) or frequency matrix 27 which constitutes the source of the processing represented by block 23.
• Block 23 corresponds in particular to the data processing and analysis phase.
• These treatments can be of several types (supervised classification, unsupervised classification, statistical notation or "scoring", regression ).
• it is unsupervised classification of data also known as automatic classification or "clustering" (Anglo-Saxon term).
• the invention particularly relates to an unsupervised classification method, the result of which is, for example, a hierarchy of partitions of the objects 24 or documents 28 according to the initial data type.
• v ⁇ v ⁇ v 2 , ..., v ⁇
• Table T has the general term t lk which represents the value taken by the variable V k on the object O 1 and is in the following form:
• the method applies on this table re-coding treatments described below, for example the discretization of quantitative variables or the recoding of qualitative variables into presence / absence descriptors.
• the qualitative and quantitative variables are transformed into binary variables which will constitute a table K of presence-absence.
• the transformation consists, for example, in a re-coding of the modalities into presence-absence descriptor vectors.
• k I ⁇ can have two meanings depending on whether it is initially qualitative variables or quantitative variables:
• k ⁇ has the following definition:
• k is the following definition: i fl if the object belongs to the segment j K j - ⁇
• CSP Socio-Professional Category
• the table of transformed data K will be of dimensions ⁇ nxp) with p> m where m is the number of variable of set V.
• Filtering eliminates unclear descriptors.
• the elimination of the descriptors will be different according to the type of the latter.
• numerical data is based on an indicator of relevance of the descriptor.
• the method according to the invention uses a structural or indicator threshold which has the particular function of defining an optimization criterion adapted to the data.
• O 1 represents the profile of the object O 1 given by the i- th row of Table K:
• the method according to the invention is of linear complexity insofar as the formulas of the type ⁇ C n , and ⁇ / (C 1, C jV ) are computable
• the invention for illustrative purposes describes the particular case f (C n , C ⁇ V ) - Min (C n , C lY ) particularly suitable for bases containing a large number of missing data and the procedures that make it possible to calculate with a linear complexity the Calculations of the type ⁇ Min (C n , C lV ).
• the method according to the invention implements, for example, the following criterion:
• the structural threshold parameter a s is calculated automatically. It is an indicator that is a function of the n 2 agreements between the objects to be classified: We can give as an example, its formulation when it represents the ratio between the arithmetic average of the agreements between all the objects on the arithmetical average of their maximal chords. :
• the criterion C a * (X) used in the invention consists, for example, in comparing the agreements between any two objects with the product of the threshold a s by their maximum agreement (which represents a percentage of the maximum agreement).
• the method of classification 36 is described in detail in FIG. 4.
• the starting point is the array of binary data whose descriptors have been filtered.
• the first step of the classification process of sorting the descriptors, 40 is based on a measure of the contribution to the criterion of each descriptor (quality of a descriptor).
• quality of a descriptor For example, descriptors are used as "generators" for structuring the population of the objects to be classified.
• a descriptor is represented by a column vector of 1 and 0 (presence-absence). We associate with this descriptor a class which is composed of the objects whose value taken is 1. We can then calculate for each class of objects C q its contribution measure Contrib (C q ) to the global criterion:
• the evaluation of the contribution of a class is of polynomial complexity. In fact, suppose that all the objects constitute a single class, it would then be necessary to calculate n 1 unitary contributions in order to determine the value of the criterion.
• This process 41 consists in particular of "fusing” progressively 42, a class generated by a descriptor 40, and a current partition (composed of several classes).
• This "merge” operation consists in determining from two classes in intersection the best operation (in the sense of the criterion) among the following 4:
• This process amounts to building step by step a partition which optimizes locally and progressively the global criterion.
• This partition 45 constitutes the input of the class aggregation process, 46, which is described in FIG.
• FIG. 5 represents an example of a functional diagram of the treatment
• the latter consists in particular of "merging" a new class generated by a descriptor X denoted C x , 50, with the current partition P consisting of K classes 51.
• the process is as follows: • Calculation of the intersections between the classes of the partition P and the class C x ,
• Class C x is considered as the class whose intersection with a partition P is to be evaluated.
• a sorted list for example, in ascending order of the indices of the objects it contains. Indeed each object is identified by a unique integer which is its index.
• a linear sorting for example the sorting by base whose reference is the following one: Cormen et al (2002), Introduction to the algorithmic, Dunod
• the vector A is equal to:
• the number of operations performed when calculating intersections 52 is equal to the number of objects in the class C x to merge with the partition P. Indeed, we look for each object of the new class if it belongs to a class of the partition P, if yes we increment the intersection counter of this class.
• R x and R y be the vectors representing classes C x and C y
• R a , R b and R c be defined by:
• CardA, CardB and CardC as the cardinals of the classes represented by R ", R b and R c .
• C x ® "break" the class C x to form the two following classes: C x - C 1 , and
• the new class C x contains the objects that can belong to the other classes of the partition P since we know, in advance, that no object of the class C y can belong to another class of the score P.
• Hierarchical aggregation method of the classes of the invention In FIG. 8, an example of an architecture of the hierarchical aggregation method of the classes in meta-classes is represented.
• This method consists, in particular, in grouping on several levels (the number of levels is to be set by the user) classes between them.
• the optimization of the global criterion passes, for example, by maximizing the links between the classes formed. In other words, the classes obtained must ultimately have negative links.
• the calculation of the links is based on the definition of the contribution Contrit ⁇ , of a pair (i, f) of objects.
• the final quality of the score obtained is given by:
• nb _cols nb _cols - 1

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• General Physics & Mathematics (AREA)
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• Image Analysis (AREA)

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• 2005
  • 2005-12-16 FR FR0512844A patent/FR2895110B1/fr active Active
• 2006
  • 2006-12-14 CN CN2006800528444A patent/CN101410831B/zh not_active Expired - Fee Related
  • 2006-12-14 US US12/097,613 patent/US8423549B2/en not_active Expired - Fee Related
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