CN107562793A - A kind of big data method for digging - Google Patents
A kind of big data method for digging Download PDFInfo
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- CN107562793A CN107562793A CN201710646448.4A CN201710646448A CN107562793A CN 107562793 A CN107562793 A CN 107562793A CN 201710646448 A CN201710646448 A CN 201710646448A CN 107562793 A CN107562793 A CN 107562793A
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Abstract
The embodiments of the invention provide a kind of big data method for digging, methods described includes：Obtain the quantity of the commodity in user browse data；The quantity of commodity in each user browse data is classified to user, determines that targeted customer gathers；The single user characteristic vector of each user in being gathered according to the targeted customer；According to the single user characteristic vector, targeted customer set is classified based on clustering algorithm, determines that hierarchic user gathers, wherein, the quantity of hierarchic user's set is equal with predetermined classification quantity.Using the embodiment of the present invention, on the one hand the data volume of computing more targetedly, can be reduced, on the other hand can exclude interference of the inhomogeneous user data for Clustering Effect, make user group's division more accurate, be easy to carry out accurate formula, personalized service according to user group's division result.
Description
Technical field
The present invention relates to data processing field, more particularly to a kind of big data method for digging.
Background technology
With society's industrialization, the continuous improvement of the level of IT application, nowadays data, which have substituted, is calculated as information calculating
Center, cloud computing, big data turn into a kind of trend and trend, including memory capacity, availability, I/O performances, data peace
All many-sides such as Quan Xing, scalability.Big data is the very huge and complicated data set of scale.Big data has 4V：Volume
(a large amount of), data volume increases continuously and healthily；Velocity (high speed), data I/O speed are faster；Variety (various), data class
Type and source variation；Value (value), there is the usable value of each side in it.How from the extracting data of magnanimity, obtain
Desired knowledge or information interested, this is to make good use of big data, and then preferably serves the requirement of social development.Cause
This, data digging method arises at the historic moment.
Data mining is born in as a subject in the 1980s, being exactly from large amount of complex from the point of view of technology
, obtain information implicit, that people do not realize in advance, having potential value in irregular, random, fuzzy data
With the process of knowledge.In big data application field, user group can be often divided into according to the various actions feature of user
If Ganlei, in order to carry out accurate formula, personalized service for the feature of customer group.Cluster is that user group is divided
A kind of mode.Cluster is by the classified process of data object, the object in same class is had very high similarity, and is made
Object height in inhomogeneity is different.Distinctiveness ratio is measured usually using distance.
But the effect divided in cluster operation to user group for user behavior feature largely according to
Rely the quality in basic data, existing user group's division based on clustering algorithm tends not to enough reflect user's well
Behavioural characteristic, the problem of cluster is inaccurate be present, it is difficult to accurate formula, personalized clothes are carried out to customer group using cluster result
Business.
The content of the invention
The purpose of the embodiment of the present invention is to provide a kind of big data method for digging, to be implemented in raising user group's division
The degree of accuracy.
In order to achieve the above object, the embodiment of the invention discloses a kind of big data method for digging, methods described to include：
Obtain the quantity of the commodity in user browse data；
The quantity of commodity in each user browse data is classified to user, determines that targeted customer gathers；
The single user characteristic vector of each user in being gathered according to the targeted customer；
According to the single user characteristic vector, targeted customer set is classified based on clustering algorithm, it is determined that classification is used
Family is gathered, wherein, the quantity of hierarchic user's set is equal with predetermined classification quantity.
Optionally, the quantity of the commodity in each user browse data of the basis is classified to user, determines target
User gathers, including：
The quantity of commodity in each user browse data is input to the good neutral net of training in advance, user classified, really
The user that sets the goal gathers.
Optionally, the quantity of the commodity in each user browse data of the basis is classified to user, determines target
User gathers, including：
The quantity of the commodity in each user browse data is clustered using the method for cluster, according to cluster result to user
Classification, determine that targeted customer gathers.
Optionally, it is described according to the single user characteristic vector, the targeted customer is gathered based on clustering algorithm and carried out
Classification, determine that hierarchic user's set includes：
High-density region user is determined according to the single user characteristic vector of each user；
Be selected as the user of initial cluster center from the high-density region user, the quantity of the initial cluster center with
The predetermined classification quantity is equal；
According to the initial cluster center, hierarchic user's set is determined based on K mean algorithms.
Optionally, it is described to be selected as initial cluster center from the high-density region user, including：
The maximum user of density parameter is selected in the high-density region user to be used as the according to the single user characteristic vector
One initial cluster center；
The farthest user of the first initial cluster center described in selected distance is initial as second from the high-density region user
Cluster centre；
The first initial cluster center described in selected distance and second initial cluster center from the high-density region user
The farthest user of the distance of set is as the 3rd initial cluster center；
The like until determining all initial cluster centers.
Optionally, methods described also includes：
Characteristic vector index in the single user characteristic vector is subjected to data normalization processing；
According to the single user characteristic vector after standardization, targeted customer set is divided based on clustering algorithm
Level, determine that hierarchic user gathers.
Optionally, the quantity of the commodity in the user browse data is the commodity in pretreated user browse data
Quantity.
Big data method for digging provided in an embodiment of the present invention, first can be classified user, be entered in a classification
Row user clustering, so as to select suitable targeted customer to carry out cluster analysis, on the one hand more targetedly it can reduce fortune
The data volume of calculation, interference of the inhomogeneous user data for Clustering Effect on the other hand can be excluded, make user group's division more
It is accurate to add, and is easy to carry out accurate formula, personalized service according to user group's division result.Certainly, any of the present invention is implemented
Product or method must be not necessarily required to reach all the above advantage simultaneously.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the schematic flow sheet of big data method for digging provided in an embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
Fig. 1 is the schematic flow sheet of big data method for digging provided in an embodiment of the present invention, and the method comprising the steps of：
S101：Obtain the quantity of the commodity in user browse data.
In scheme provided in an embodiment of the present invention, user browse data can be obtained in navigation patterns corresponding to user
In commodity quantity.The Service Operation chamber of commerce stores the navigation patterns of user, and all behaviors of the user in service can be remembered
Record is got off.
In a kind of specific implementation, the quantity of the commodity in the user browse data is that pretreated user browses
The quantity of commodity in data.
It is understood that pretreatment include detection user browse data in commodity quantity integrality with it is consistent
Property, Denoising disposal is carried out to each data.So make it that the result of subsequent classification is more accurate.
S102：The quantity of commodity in each user browse data is classified to user, determines that targeted customer collects
Close.
In a specific implementation of the invention, the quantity of the commodity in each user browse data of basis is to user
Classified, determine that targeted customer gathers, can include：
The quantity of commodity in each user browse data is input to the good neutral net of training in advance, user classified, really
The user that sets the goal gathers.
It is understood that neutral net（Neural Networks, NN）It is by substantial amounts of, simple processing unit（Claim
For neuron）The complex networks system for widely interconnecting and being formed, it reflects many essential characteristics of human brain function, is
One highly complex non-linear dynamic learning system.
Neutral net is mainly reflected in the following aspects to the huge attraction of people：
1. Serial Distribution Processing.
2. height robustness and fault-tolerant ability.
3. distribution storage and learning ability.
4. it can fully approach the non-linear relation of complexity.
It is and as follows to the training method substantially process of neutral net：Training sample set is collected, the number that training sample is concentrated
According to neutral net is input to, neutral net is trained, until the output of neutral net reaches default expectation.
In embodiments of the present invention, neutral net can classify user, and one or more classification can be selected to make respectively
Gather for targeted customer.
In an embodiment of the invention, the quantity of the commodity in each user browse data of the basis is to user
Classified, determine that targeted customer gathers, can include：
The quantity of the commodity in each user browse data is clustered using the method for cluster, according to cluster result to user
Classification, determine that targeted customer gathers.
It should be noted that in scheme provided in an embodiment of the present invention, mentioned cluster, refer to physics or abstract
The set of object is divided into the process for the multiple classes being made up of similar object.It is one group of data object by clustering generated cluster
Set, these objects are similar each other to the object in same cluster, different with the object in other clusters.Cluster analysis is also known as group point
Analysis, it is research（Sample or index）A kind of statistical analysis technique of classification problem.
In embodiments of the present invention, by the quantity of the commodity in each user browse data, user can be divided into not
Same set, one or more set are selected as targeted customer's set.Specifically, user can be browsed number when cluster
The quantity of commodity in is polymerized to a set in user corresponding to a number range.Certainly, it is not limited to that also have
Hierarchical method, method based on density etc..
In embodiments of the present invention, the quantity of the commodity in user browse data can be less than the first predetermined threshold value, really
It is first kind user to determine user；The quantity of commodity in user browse data is not less than the first predetermined threshold value, default less than second
Threshold value, it is the second class user to determine user；If the quantity of the commodity in user browse data is not less than the second predetermined threshold value, it is less than
Three predetermined threshold values, it is the 3rd class user to determine user, the like, until by all with being classified per family.
S103：The single user characteristic vector of each user in being gathered according to the targeted customer.
It is understood that can according to the quantity of predefined action data, the effectiveness data of predefined action data, generation when
Between, residing time interval etc. determine single user characteristic vector.Effectiveness data of predefined action data including predefined action and pre-
Determine the generation time of behavior.Same user can have a plurality of predefined action data, include the generation time of the predefined action data
With effectiveness data.Predefined action data also include predetermined condition mark and effectiveness deduction data.Effectiveness deduction data can be by
Predetermined condition is met in predefined action and caused deduction effectiveness, such as makes effectiveness data than amount that standard effectiveness data reduce
Deng.It can judge whether predefined action conforms to a predetermined condition by the predetermined condition mark of predefined action data, will can meet
The predefined action data of the predefined action of predetermined condition are referred to as the first predefined action data.
In embodiments of the present invention, single user characteristic vector can include first eigenvector index, second feature vector
Index, third feature are to figureofmerit.Specifically, can be according to the quantity and predefined action number of the first predefined action data of user
According to the ratio of quantity determine the first eigenvector index of user；Determine the effectiveness deduction of each predefined action data of user
The ratio of data and effectiveness data, and ratio is taken into average, determine the second feature of user to figureofmerit；According to the effectiveness of user
The ratio for data sum and the effectiveness data sum of deducting, determine the third feature of user to figureofmerit.
By multiple characteristic vector index constitutive characteristics vector, sensitivity of the user to predetermined condition can be accurately depicted
Degree, so as to which the significant user's classification for embodying user for predetermined condition sensitivity difference in cluster calculation, can be obtained, just
Targetedly applied in being carried out based on hierarchic user, user is carried out and targetedly serviced.
S104：According to the single user characteristic vector, targeted customer set is classified based on clustering algorithm, really
Determine hierarchic user's set, wherein, the quantity of hierarchic user's set is equal with predetermined classification quantity.
In one embodiment of the invention, according to the single user characteristic vector, based on clustering algorithm to the target
User's set is classified, and determines that hierarchic user gathers, including：
Step A1：High-density region user is determined according to the single user characteristic vector of each user.
It should be noted that can centered on the single user characteristic vector point of user point, it is determined that including predetermined quantity
The radius in the region of other users single user characteristic vector point, if radius is less than predetermined threshold, then it is assumed that user is high density area
Domain user.Can also centered on the single user characteristic vector point of user point, determine other users in the region of predetermined radii
The quantity of single user characteristic vector point, if the quantity reaches predetermined quantity, then it is assumed that user is high-density region user.
Step A2：Initial cluster center is selected as from the high-density region user, the initial cluster center
Quantity is equal with the predetermined classification quantity.
For example, the user during if desired targeted customer is gathered is divided into Pyatyi by cluster, then need in high-density region
5 initial cluster centers of middle selection.
Step A3：According to the initial cluster center, hierarchic user's set is determined based on K mean algorithms.
K mean algorithms, K-means algorithms can be referred to as, be a kind of simplest clustering algorithm, the purpose of algorithm is to make
The error sum of squares of each sample and place class average reaches minimum.
Generally, highdensity data area can be separated by the data area of low-density, and these are located at density regions
Data point be generally known as isolated point.Current existing clustering algorithm is mostly randomly to choose initial cluster center, and this is neglected
Depending on the distribution situation of data, because the selection of initial cluster center in K mean algorithms can have an impact to result, therefore at random
Selection initial cluster center can greatly influence final Clustering Effect.Pass through the method in the embodiment of the present invention, Neng Goubao
Card initial cluster center is high-density region user, avoids causing user to be classified using some Standalone customers as initial cluster center
Inaccuracy.
In an embodiment of the invention, it is described to be selected as from the high-density region user in initial clustering
The heart, it can include：
The maximum user of density parameter is selected in the high-density region user to be used as the according to the single user characteristic vector
One initial cluster center；
The farthest user of the first initial cluster center described in selected distance is initial as second from the high-density region user
Cluster centre；
The first initial cluster center described in selected distance and second initial cluster center from the high-density region user
The farthest user of the distance of set is as the 3rd initial cluster center；
The like until determining all initial cluster centers.
In K-means algorithms, calculate the distance of each k initial cluster center of data point distance, by data point and with
Its closest initial cluster center point is classified as a cluster, now judges whether to reach the condition i.e. cluster centre for stopping cluster
No longer change, exited if stop condition is met, otherwise update the cluster centre point of each cluster, take the interior institute of each cluster a little
Average as new cluster centre, circulation performs above-mentioned calculating process, until cluster centre no longer changes.By so
Method, can complete cluster operation, obtain hierarchic user's set.It should be noted that distance mentioned here can use
The distance between two points calculation formula is calculated.
By such method, the farthest user of mutual distance can be selected in high-density region user as initial poly-
Class center, it on the one hand can exclude to select Standalone customers to impact cluster result as initial cluster center, on the other hand
Because the farthest initial cluster center point of mutual distance is more more representative than what is randomly selected, obtained by the method first
Beginning cluster centre is also more representative, can optimize Clustering Effect, obtains more representational user's classification results.
In another specific implementation of the present invention, method can also include：
Characteristic vector index in the single user characteristic vector is subjected to data normalization processing；
According to the single user characteristic vector after standardization, targeted customer set is divided based on clustering algorithm
Level, determine that hierarchic user gathers.
In embodiments of the present invention, characteristic vector index is subjected to data normalization processing, different dimensions can be eliminated
Influence to cluster result.
Using the embodiment of the present invention, first user can be classified, user clustering is carried out in a classification, so as to
The suitable targeted customer of enough selections carries out cluster analysis, on the one hand more targetedly, can reduce the data volume of computing, the opposing party
Face can exclude interference of the inhomogeneous user data for Clustering Effect, make user group division it is more accurate, be easy to according to
Family colony division result carries out accurate formula, personalized service.
It should be noted that herein, such as first and second or the like relational terms are used merely to a reality
Body or operation make a distinction with another entity or operation, and not necessarily require or imply and deposited between these entities or operation
In any this actual relation or order.Moreover, term " comprising ", "comprising" or its any other variant are intended to
Nonexcludability includes, so that process, method, article or equipment including a series of elements not only will including those
Element, but also the other element including being not expressly set out, or it is this process, method, article or equipment also to include
Intrinsic key element.In the absence of more restrictions, the key element limited by sentence "including a ...", it is not excluded that
Other identical element also be present in process, method, article or equipment including the key element.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all
Any modification, equivalent substitution and improvements made within the spirit and principles in the present invention etc., are all contained in protection scope of the present invention
It is interior.
Claims (7)
1. a kind of big data method for digging, it is characterised in that methods described includes：
Obtain the quantity of the commodity in user browse data；
The quantity of commodity in each user browse data is classified to user, determines that targeted customer gathers；
The single user characteristic vector of each user in being gathered according to the targeted customer；
According to the single user characteristic vector, targeted customer set is classified based on clustering algorithm, it is determined that classification is used
Family is gathered, wherein, the quantity of hierarchic user's set is equal with predetermined classification quantity.
2. according to the method for claim 1, it is characterised in that the number of the commodity in each user browse data of basis
Amount is classified to user, determines that targeted customer gathers, including：
The quantity of commodity in each user browse data is input to the good neutral net of training in advance, user classified, really
The user that sets the goal gathers.
3. according to the method for claim 1, it is characterised in that the number of the commodity in each user browse data of basis
Amount is classified to user, determines that targeted customer gathers, including：
The quantity of the commodity in each user browse data is clustered using the method for cluster, according to cluster result to user
Classification, determine that targeted customer gathers.
4. according to the method for claim 1, it is characterised in that it is described according to the single user characteristic vector, based on cluster
Algorithm is classified to targeted customer set, determines that hierarchic user's set includes：
High-density region user is determined according to the single user characteristic vector of each user；
Be selected as initial cluster center from the high-density region user, the quantity of the initial cluster center with it is described pre-
Surely it is equal to be classified quantity；
According to the initial cluster center, hierarchic user's set is determined based on K mean algorithms.
5. according to the method for claim 4, it is characterised in that described to be selected as from the high-density region user just
Beginning cluster centre, including：
The maximum user of density parameter is selected in the high-density region user to be used as the according to the single user characteristic vector
One initial cluster center；
The farthest user of the first initial cluster center described in selected distance is initial as second from the high-density region user
Cluster centre；
The first initial cluster center described in selected distance and second initial cluster center from the high-density region user
The farthest user of the distance of set is as the 3rd initial cluster center；
The like until determining all initial cluster centers.
6. according to the method for claim 1, it is characterised in that methods described also includes：By the single user characteristic vector
In characteristic vector index carry out data normalization processing；
According to the single user characteristic vector after standardization, targeted customer set is divided based on clustering algorithm
Level, determine that hierarchic user gathers.
7. according to the method for claim 1, it is characterised in that the quantity of the commodity in the user browse data is pre- place
The quantity of the commodity in user browse data after reason.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110288365A (en) * | 2018-03-19 | 2019-09-27 | 北京京东尚科信息技术有限公司 | Data processing method and system, computer system and computer readable storage medium storing program for executing |
| CN112001761A (en) * | 2020-08-31 | 2020-11-27 | 上海博泰悦臻电子设备制造有限公司 | User classification method and related device |
| CN112765468A (en) * | 2021-01-23 | 2021-05-07 | 珠海金智维信息科技有限公司 | Personalized user service customization method and device |
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Application publication date: 20180109 |