CN102982077B - User data disposal route and device - Google Patents
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Abstract
The invention provides a kind of user data disposal route and device, this user data disposal route comprises: obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to multiple user's sample data, the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book; Pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.User data disposal route provided by the invention and device, realize standardization by state code book, User Status that is unitized, low dimension represent, using the unified benchmark of state code book as degree of depth customer analysis, substantially increase the treatment effect of user data.
Description
Technical field
The present invention relates to data processing technique, particularly relate to a kind of user data disposal route and device.
Background technology
In order to effectively grasp user profile and User Status change, operator needs to collect a large number of users data and analyzes, user data generally includes the many aspects such as essential information, social information, preference information, use information and contact information, and each aspect is one group of dimension.Therefore, for each user, usually can form a hundreds of, even the long vector of thousands of dimensions describes this user and behavioural characteristic thereof.
Along with the continuous growth of number of users, user data also increases in explosion type.Because the dimension of user data is more, and relevance between each dimension data is comparatively sparse, necessarily increases the analysis difficulty of user data, needs the disposal route of a kind of magnanimity, higher-dimension user data badly.
Summary of the invention
The invention provides a kind of user data disposal route and device, to improve the treatment effect to user data.
The present invention first aspect provides a kind of user data disposal route, comprising:
Obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to described multiple user's sample data, the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book;
Pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in described multiple state code book, to carry out customer analysis according to be mapped to state code book.
Another aspect of the present invention provides a kind of user data treating apparatus, comprising:
State code book generation module, for obtaining multiple user's sample data, carrying out dimension-reduction treatment respectively to described multiple user's sample data, training the user's sample data after multiple dimension-reduction treatment, generating multiple state code book;
Processing module, for pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in described multiple state code book, to carry out customer analysis according to be mapped to state code book.
As shown from the above technical solution, user data disposal route provided by the invention and device, obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to multiple user's sample data, user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book, pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.By the generation of state code book, standardization can be realized by state code book, User Status that is unitized, low dimension represents, using the unified benchmark of state code book as degree of depth customer analysis, again pending user data is carried out dimensionality reduction and normalized, by multidimensional and the sparse user data of dimensional information is treated as the data being convenient to analyze, again the user data after this process is mapped to a state code book, be mapped to state code book can realize customer analysis by this, substantially increase the treatment effect of user data.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
A kind of user data process flow figure that Fig. 1 provides for the embodiment of the present invention;
The another kind of user data process flow figure that Fig. 2 provides for the embodiment of the present invention;
A kind of user data treating apparatus structural representation that Fig. 3 provides for the embodiment of the present invention;
The another kind of user data treating apparatus structural representation that Fig. 4 provides for the embodiment of the present invention.
Embodiment
A kind of user data process flow figure that Fig. 1 provides for the embodiment of the present invention.As shown in Figure 1, the user data disposal route that the present embodiment provides the specifically user data that can be applied to operator collects processes, can be performed by user data treating apparatus, this user data treating apparatus can be realized by mode that is soft and/or hardware.
The user data disposal route that the present embodiment provides specifically comprises:
Step 10, obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to multiple user's sample data, the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book;
Particularly, user's sample data is specially the user's historical data collected, the user's sample data got can for the one-dimensional vector data of carrying out identifying with user and time, this user's sample data specifically comprises multiple dimension, and dimensional information specifically can comprise user basic information, customer information, account information, product information, social information, hobby preference, use information, contact information, payment information, arrearage information etc.For order business, dimensional information included by user's sample data is for ordering instance identification, year, month, economize and divide, area, networking districts and cities, customer ID, customer type, client's classification identifies, type of credential, passport NO., account identification, type of service, brand, major product identifies, order example state, way of paying, folk-urban typology, development people, channel mark, channel type, network access mode, access code, at net duration, shut down duration, whether current period new development, whether the current period increases newly, terminal models, group identifies, air time first, whether last issue enters an item of expenditure in the accounts, whether zero call, whether turn mutually, whether surf the Net, whether note, order note identification, type of service, pre-payment identifies, sell number, terminal device model, Terminal Equipment brand, guarantee type, reservation number, the reservation date, business hall identifies, value-added service type, value-added service development people, value-added service Developing channel identifies, service time first, whether experience, whether repeat to experience, to enter an item of expenditure in the accounts expense, basic monthly rent, set meal monthly rent, other monthly rent, (hierarchical fee item, conventional about 100) mobile service service condition (hierarchical business service condition, duration/number of times etc., conventional about 100-200 item), fixed telephone service service condition (about 50), the service conditions such as flow/internet/note/value-added service, initial user's credit, interim credit rating, current credit, etc..As can be seen here, user's sample data relates to a lot of dimension, but not all dimensional information is all useful.
The quantity of user's sample data can be arranged according to the actual needs, such as can for hundreds of is to several thousand.The quantity of user's sample data is more, and the state code book generated according to this more can reflect user's situation, but processing procedure is more complicated relatively, and the processing time is longer.
Specifically can need to carry out dimension-reduction treatment to user's sample data according to customer analysis, by the method such as feature selecting or singular value tap, redundancy or unessential dimensional information can be deleted or merging, under the prerequisite retaining maximum discrimination information, reduce vector dimension, namely reduce the dimension of user's sample data.If such as consider customer analysis demand from operational angle, the object of dimensionality reduction, normalization and code book carries out general data mining and monitoring, therefore the information such as personal information and fine-grained data (as refinement fee item) such as year, the moon, type of credential, passport NO., order note identification, reservation number, development people are not paid close attention to, can delete, or be aggregated into more high-level, review relative granularity data again when needing.If consider customer analysis demand from technical standpoint, be then more carry out selecting from data self aspect, merger, to reach the object of dimensionality reduction.Complete vector may reach 800-1000 dimension, can carry out the Feature Selection (dimensionality reduction) in group by its original packet (as used information).Need relevant dimension digitizing, as the unified numeral be converted between 0-100, then adopt feature selecting algorithm, obtain the result after dimensionality reduction by the dimension length (as being set as 50 dimensions) of expection.Particularly, identical dimension-reduction treatment method is adopted to all user's sample datas, to ensure the correspondence of the user's sample data after dimension-reduction treatment.
Again the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book, specifically clustering algorithm can be adopted to the training of user's sample data, as NNCA algorithm, k-means algorithm or K-Medioids algorithm etc., also sorting algorithm can be adopted, as sorting algorithm or decision Tree algorithms etc. based on information entropy, also not only clustering algorithm can be adopted but also adopt sorting algorithm to train the user's sample data after dimensionality reduction.State code book is specially and represents the standardization of User Status, the vector of low dimension, in order to represent the state of user at section or time point sometime.A limited state codebook set can be used to set up the standardization state of user's special time period/time point, multiple state code book can be formed state code book sequence U=(u1, u2,, un), such as, by user in 1 year the state code book of 12 months form state code book sequence, then n=12.
Below specifically the form of state code book is described, such as:
Code book 1:(3,20,50,5,8 ...)
Code book 2:(10,20,50,10,20 ...)
Code book N:(100,80,90,100,100 ...)
Step 20, pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.
The user data that the user of the concrete Water demand of pending user data is corresponding, this user data can, for the one-dimensional vector data of being undertaken identifying by the time, also can be also specifically the data of this certain time period of user.When user data is the data of certain time period, then this time period can be needed to be divided into multiple timeslice according to analysis, then according to timeslice, user data be divided, to generate multiple one-dimensional vector data.Pending user data is carried out dimension-reduction treatment, and the method that this dimension-reduction treatment adopts specifically can be identical with the method adopted when carrying out dimension-reduction treatment to user's sample data.User data after dimension-reduction treatment is normalized, to make the dimensional information of user data, there is comparison.User data after normalized is mapped to a state code book in multiple state code book, default Mapping standard can be adopted to carry out mapping process, this default Mapping standard is specifically corresponding with the method adopted during training generation state code book, can correctly be mapped on unique state code book to make the user data after normalized.In code book training process, employ normalization scheme, in identification/use, real data/test data will adopt identical processing procedure, could mate with code book.As, by unified for all dimensions numeral be converted between 0-100, concrete mode can be selected according to data characteristics, as state (Yes/No), can be converted to 100 and 0, the duration of call can by distribution situation, be mapped between 0-100, as 0 minute is mapped as 0, within more than 2000 minutes, be mapped as 100, centre can subsection compression.Can linear mapping be done, also can do Nonlinear Mapping, relevant dimension is transformed in specific numerical range.
Then can carry out customer analysis according to be mapped to state code book, as hived off, early warning or other behavior prediction alanysis etc.
The user data disposal route that the present embodiment provides, obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to multiple user's sample data, user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book, pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.By the generation of state code book, standardization can be realized by state code book, User Status that is unitized, low dimension represents, using the unified benchmark of state code book as degree of depth customer analysis, again pending user data is carried out dimensionality reduction and normalized, by multidimensional and the sparse user data of dimensional information is treated as the data being convenient to analyze, again the user data after this process is mapped to a state code book, be mapped to state code book can realize customer analysis by this, substantially increase the treatment effect of user data.
The another kind of user data process flow figure that Fig. 2 provides for the embodiment of the present invention.As shown in Figure 2, in the present embodiment, in step 10, the user's sample data after multiple dimension-reduction treatment is trained, generates multiple state code book, specifically can comprise the steps:
User's sample data after multiple dimension-reduction treatment is carried out classification process, the user's sample data after multiple classification process is carried out clustering processing, generates multiple state code book.
In the present embodiment, in step 20, pending user data is carried out dimension-reduction treatment, the user data after dimension-reduction treatment is normalized, specifically can comprise the steps:
Know that pending user data is the user data in certain hour section if judge, then certain hour section is divided at least two timeslices, pending user data is divided into the time dimension vector of each timeslice in corresponding at least two timeslices of difference, each time dimension vector is carried out dimension-reduction treatment, the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized.
When the user data that pending user data is certain hour section, then the timeslice comprised according to this time period divides this user data, to reduce the complicacy of user data.And, by timeslice, user data is divided, for providing basis to User Status change monitoring.
Pending user data is divided into the time dimension vector of each timeslice in corresponding at least two timeslices of difference, the i.e. corresponding time dimension vector of each timeslice, each time dimension vector is carried out dimension-reduction treatment, again the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized, when the dimensional information comprised in time dimension vector is non-quantitation data, quantification treatment is carried out to this dimensional information, is conducive to the handlability improving dimension vector.
In the present embodiment, in step 20, after the user data after normalized being mapped to a state code book in multiple state code book, specifically can also comprise the steps:
At least one User Status of step 30, at least one state code book that the user data obtaining same user is mapped to and user, generate Status Change mapping relations according at least one state code book and at least one User Status, according to Status Change mapping relations, condition monitoring is carried out to user.
The user data of same user within a period of time may be mapped to multiple state code book, and during this period of time, user may have multiple User Status, then form the Status Change mapping relations of state code book and User Status, to be noted abnormalities by these Status Change mapping relations, realize carrying out status monitoring to user, for the User Status fluctuation that amplitude is larger, can according to problem existing in this fluctuation concrete analysis operation.Status Change mapping relations specifically can adopt Markov model or finite state machine to realize.
In the present embodiment, in step 20, after the user data after normalized being mapped to a state code book in multiple state code book, specifically can also comprise the steps:
Carry out customer analysis according to be mapped to state code book, wherein, the analytical approach that customer analysis adopts comprises following at least one: cluster, classification and association analysis.
Customer analysis specifically can comprise tenant group, fine integral method, warning against losing customers, reason are detected and behavior prediction etc., can adopt corresponding analytical approach according to concrete analysis demand.
A kind of user data treating apparatus structural representation that Fig. 3 provides for the embodiment of the present invention.As shown in Figure 3, the user data treating apparatus that the present embodiment provides specifically can realize each step of the user data disposal route that any embodiment of the present invention provides, and does not repeat them here.The concrete figure of user data treating apparatus that the present embodiment provides comprises state code book generation module 11 and processing module 12.State code book generation module 11, for obtaining multiple user's sample data, carries out dimension-reduction treatment respectively to multiple user's sample data, trains the user's sample data after multiple dimension-reduction treatment, generates multiple state code book.Processing module 12 is for carrying out dimension-reduction treatment by pending user data, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.
The user data treating apparatus that the present embodiment provides, state code book generation module 11 obtains multiple user's sample data, respectively dimension-reduction treatment is carried out to multiple user's sample data, the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book.Pending user data is carried out dimension-reduction treatment by processing module 12, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in multiple state code book, to carry out customer analysis according to be mapped to state code book.By the generation of state code book, standardization can be realized by state code book, User Status that is unitized, low dimension represents, using the unified benchmark of state code book as degree of depth customer analysis, again pending user data is carried out dimensionality reduction and normalized, by multidimensional and the sparse user data of dimensional information is treated as the data being convenient to analyze, again the user data after this process is mapped to a state code book, be mapped to state code book can realize customer analysis by this, substantially increase the treatment effect of user data.
The another kind of user data treating apparatus structural representation that Fig. 4 provides for the embodiment of the present invention.As shown in Figure 4, in the present embodiment, state code book generation module 11 can also be used for the user's sample data after by multiple dimension-reduction treatment and carry out classification process, the user's sample data after multiple classification process is carried out clustering processing, generates multiple state code book.
In the present embodiment, if for judging, processing module 12 also knows that pending user data is the user data in certain hour section, then certain hour section is divided at least two timeslices, pending user data is divided into the time dimension vector of each timeslice in corresponding at least two timeslices of difference, each time dimension vector is carried out dimension-reduction treatment, the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized.
When the user data that pending user data is certain hour section, then the timeslice comprised according to this time period divides this user data, to reduce the complicacy of user data.And, by timeslice, user data is divided, for providing basis to User Status change monitoring.
Pending user data is divided into the time dimension vector of each timeslice in corresponding at least two timeslices of difference, the i.e. corresponding time dimension vector of each timeslice, each time dimension vector is carried out dimension-reduction treatment, again the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized, when the dimensional information comprised in time dimension vector is non-quantitation data, quantification treatment is carried out to this dimensional information, is conducive to the handlability improving dimension vector.
In the present embodiment, this user data treating apparatus can also comprise User Status monitoring module 13 further, at least one User Status of at least one state code book that User Status monitoring module 13 is mapped to for the user data obtaining same user and user, generate Status Change mapping relations according at least one state code book and at least one User Status, according to Status Change mapping relations, condition monitoring is carried out to user.
Realize carrying out status monitoring to user, for the User Status fluctuation that amplitude is larger, can according to problem existing in this fluctuation concrete analysis operation.
In the present embodiment, this user data treating apparatus can also comprise analysis module 14 further, analysis module 14 is for carrying out customer analysis according to be mapped to state code book, and wherein, the analytical approach that customer analysis adopts comprises following at least one: cluster, classification and association analysis.
The user data disposal route that the embodiment of the present invention provides and device, by the generation of state code book, provide unified standardized User Status to represent, depth analysis based on the state code book of the low dimension of this standardization excavates, realize customer analysis and User Status is monitored, decrease the various problems brought based on high-dimensional vector analysis, the such as high-dimensional Sparse Problem brought, what excessively flexible and random dimensionality reduction caused repeats processing, repeat, time consuming procedures found, depth analysis lacks the problems such as unified data base, substantially increase the treatment effect of user data.
One of ordinary skill in the art will appreciate that: all or part of step realizing said method embodiment can have been come by the hardware that programmed instruction is relevant, aforesaid program can be stored in a computer read/write memory medium, this program, when performing, performs the step comprising said method embodiment; And aforesaid storage medium comprises: ROM, RAM, magnetic disc or CD etc. various can be program code stored medium.
Last it is noted that above embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.
Claims (8)
1. a user data disposal route, is characterized in that, comprising:
Obtain multiple user's sample data, respectively dimension-reduction treatment is carried out to described multiple user's sample data, the user's sample data after multiple dimension-reduction treatment is trained, generate multiple state code book;
Pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in described multiple state code book, to carry out customer analysis according to be mapped to state code book;
Pending user data is carried out dimension-reduction treatment, the user data after dimension-reduction treatment is normalized, comprises:
Know that described pending user data is the user data in certain hour section if judge, then described certain hour section is divided at least two timeslices, described pending user data is divided into the time dimension vector of each timeslice at least two timeslices described in difference correspondence, each time dimension vector is carried out dimension-reduction treatment, the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized.
2. user data disposal route according to claim 1, is characterized in that, trains the user's sample data after multiple dimension-reduction treatment, generates multiple state code book, comprising:
User's sample data after described multiple dimension-reduction treatment is carried out classification process, the user's sample data after multiple classification process is carried out clustering processing, generates described multiple state code book.
3. user data disposal route according to claim 1, is characterized in that, after the user data after normalized being mapped to a state code book in described multiple state code book, also comprises:
At least one state code book that the user data obtaining same user is mapped to and at least one User Status of described user, generate Status Change mapping relations according at least one state code book described and at least one User Status described, according to described Status Change mapping relations, condition monitoring is carried out to described user.
4. user data disposal route according to claim 1, is characterized in that, after the user data after normalized being mapped to a state code book in described multiple state code book, also comprises:
Carry out customer analysis according to be mapped to state code book, wherein, the analytical approach that described customer analysis adopts comprises following at least one: cluster, classification and association analysis.
5. a user data treating apparatus, is characterized in that, comprising:
State code book generation module, for obtaining multiple user's sample data, carrying out dimension-reduction treatment respectively to described multiple user's sample data, training the user's sample data after multiple dimension-reduction treatment, generating multiple state code book;
Processing module, for pending user data is carried out dimension-reduction treatment, user data after dimension-reduction treatment is normalized, user data after normalized is mapped to a state code book in described multiple state code book, to carry out customer analysis according to be mapped to state code book;
If for judging, described processing module also knows that described pending user data is the user data in certain hour section, then described certain hour section is divided at least two timeslices, described pending user data is divided into the time dimension vector of each timeslice at least two timeslices described in difference correspondence, each time dimension vector is carried out dimension-reduction treatment, the time dimension vector after each dimension-reduction treatment is carried out quantification treatment and normalized.
6. user data treating apparatus according to claim 5, it is characterized in that: described state code book generation module is also for carrying out classification process by the user's sample data after described multiple dimension-reduction treatment, user's sample data after multiple classification process is carried out clustering processing, generates described multiple state code book.
7. user data treating apparatus according to claim 5, is characterized in that, also comprise:
User Status monitoring module, at least one state code book that user data for obtaining same user is mapped to and at least one User Status of described user, generate Status Change mapping relations according at least one state code book described and at least one User Status described, according to described Status Change mapping relations, condition monitoring is carried out to described user.
8. user data treating apparatus according to claim 5, is characterized in that, also comprise:
Analysis module, for carrying out customer analysis according to be mapped to state code book, wherein, the analytical approach that described customer analysis adopts comprises following at least one: cluster, classification and association analysis.
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| CN104077303B (en) * | 2013-03-28 | 2017-07-28 | 国际商业机器公司 | Method and apparatus for data to be presented |
| CN105320666B (en) * | 2014-06-16 | 2018-12-25 | 中国银联股份有限公司 | The data aggregation method of more data sets |
| CN104484341A (en) * | 2014-11-24 | 2015-04-01 | 北京奇虎科技有限公司 | Method and device for dynamic analysis of data indexes |
| CN107203772B (en) * | 2016-03-16 | 2020-11-06 | 创新先进技术有限公司 | User type identification method and device |
| CN107818376A (en) * | 2016-09-13 | 2018-03-20 | 中国电信股份有限公司 | Customer loss Forecasting Methodology and device |
| CN106571909A (en) * | 2016-10-31 | 2017-04-19 | 中国联合网络通信集团有限公司 | Data encryption method and device |
| CN109325167B (en) * | 2017-07-31 | 2022-02-18 | 株式会社理光 | Feature analysis method, device, equipment and computer-readable storage medium |
| CN107579866B (en) * | 2017-10-25 | 2019-05-10 | 重庆电子工程职业学院 | A kind of business and Virtual Service intelligent Matching method of wireless dummyization access autonomous management network |
| CN111126103B (en) * | 2018-10-30 | 2023-09-26 | 百度在线网络技术（北京）有限公司 | Method and device for judging life stage state of user |
| CN111581499B (en) * | 2020-04-21 | 2023-04-28 | 北京龙云科技有限公司 | Data normalization method, device, equipment and readable storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102567391A (en) * | 2010-12-20 | 2012-07-11 | 中国移动通信集团广东有限公司 | Method and device for building classification forecasting mixed model |
| CN102609854A (en) * | 2011-01-25 | 2012-07-25 | 青岛理工大学 | Client partitioning method and device based on unified similarity calculation |
Family Cites Families (1)
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|---|---|---|---|---|
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-
2012
- 2012-10-30 CN CN201210425436.6A patent/CN102982077B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102567391A (en) * | 2010-12-20 | 2012-07-11 | 中国移动通信集团广东有限公司 | Method and device for building classification forecasting mixed model |
| CN102609854A (en) * | 2011-01-25 | 2012-07-25 | 青岛理工大学 | Client partitioning method and device based on unified similarity calculation |
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