WO2021038801A1 - パターン抽出プログラム、装置、及び方法 - Google Patents

パターン抽出プログラム、装置、及び方法 Download PDF

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Publication number
WO2021038801A1
WO2021038801A1 PCT/JP2019/033949 JP2019033949W WO2021038801A1 WO 2021038801 A1 WO2021038801 A1 WO 2021038801A1 JP 2019033949 W JP2019033949 W JP 2019033949W WO 2021038801 A1 WO2021038801 A1 WO 2021038801A1
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Prior art keywords
combination
evaluation value
combination pattern
pattern
value
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Ceased
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PCT/JP2019/033949
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English (en)
French (fr)
Japanese (ja)
Inventor
岩下　洋哲
啓介 後藤
耕太郎 大堀
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Fujitsu Ltd
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Fujitsu Ltd
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Application filed by Fujitsu Ltd filed Critical Fujitsu Ltd
Priority to EP19943242.8A priority Critical patent/EP4024312A4/en
Priority to JP2021541906A priority patent/JP7168095B2/ja
Priority to PCT/JP2019/033949 priority patent/WO2021038801A1/ja
Priority to CN201980099698.8A priority patent/CN114287017A/zh
Publication of WO2021038801A1 publication Critical patent/WO2021038801A1/ja
Priority to US17/671,471 priority patent/US20220172235A1/en
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0204—Market segmentation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/40—Data acquisition and logging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries

Definitions

  • the disclosed technology relates to a pattern extraction program, a pattern extraction device, and a pattern extraction method.
  • Segmentation is performed to subdivide a group of customers by combining attributes according to marketing needs.
  • Each set of subdivided customers is called a “segment”, and the customers contained in each segment have common attributes. Segment information is used to narrow down business targets and use different business strategies.
  • the disclosure technology aims to extract a combination pattern of attribute values that can obtain useful segment information from a marketing point of view.
  • the disclosed technique acquires sample set data associated with data item values for each of a plurality of data items and label information for a predetermined event. Further, the disclosed technique acquires a plurality of combination patterns, each of which is a combination of one or more of the data item values. Then, the disclosed technique determines an evaluation value for each of the plurality of combination patterns. The evaluation value is determined based on the number of samples satisfying the combination pattern among the samples shown in the sample set data and the ratio of the samples satisfying the combination pattern whose label information shows a predetermined value. To. Further, the disclosed technique extracts a combination pattern corresponding to an evaluation value that is the maximum value in the evaluation value for each of the plurality of combination patterns.
  • it has the effect of being able to extract a combination pattern of attributes that can obtain information on segments that are useful from a marketing perspective.
  • sample set data is input to the pattern extraction device 10.
  • the sample set data is data indicating a set of samples associated with a data item value for each of a plurality of data items and label information related to a predetermined event.
  • "multiple data items” are the attributes of the customer, and can be, for example, gender, age, unmarried / married, occupation, and the like.
  • the "data item value” in this case is an attribute value for each attribute.
  • the attribute "gender” is male / female
  • the attribute "age” is in the 20s / 30s / 40s / ...
  • the attribute "unmarried / married” is unmarried / married
  • the attribute "occupation" Can be a company employee / self-employed / ...
  • label information regarding a predetermined event is information indicating whether or not the reaction is as expected in response to an external action.
  • a sample that responds to an action as expected is called a "success example”
  • a sample that does not respond as expected is called a “failure example”
  • label information indicating success or failure is associated with each sample. It is assumed that
  • the pattern extraction device 10 executes a pattern extraction process, extracts segments from sample set data, and outputs them.
  • the “segment” represents a group of samples having the same high success rate of actions by a combination pattern of attribute values that each of the samples has in common.
  • white plus (+) and minus ( ⁇ ) marks represent each sample, the sample represented by plus is a successful example, and the sample represented by minus is a failure example.
  • the set of samples having a common combination pattern of certain attribute values is a “segment”.
  • the ratio of successful cases among the samples included in the segment is called the "success rate" of the segment.
  • the success rate of segment 1 is "50%".
  • the number of samples included in segment 2 is 9, of which 3 are successful examples, so that segment 2 is successful. The rate is "33%”.
  • the pattern extraction device 10 includes a sample acquisition unit 12, a combination pattern acquisition unit 14, a determination unit 16, and an extraction unit 18.
  • the sample acquisition unit 12 acquires the sample set data input to the pattern extraction device 10 and hands it over to the combination pattern acquisition unit 14.
  • the combination pattern acquisition unit 14 acquires a combination of one or more attribute values selected from a plurality of attribute values of each sample included in the sample set data as a combination pattern. Specifically, the combination pattern acquisition unit 14 adds and deletes attribute values of other attributes to the attribute values selected from the initial attribute value set, which is a set of attribute values initially selected, thereby creating a combination pattern. get.
  • the initial attribute value set can be an attribute in which the attribute values have an exclusive relationship, for example, a set of attribute values such as gender and unmarried / married (for example, ⁇ male, female, unmarried, married ⁇ ).
  • the combination pattern acquisition unit 14 passes the acquired combination pattern of the attribute values to the determination unit 16.
  • the determination unit 16 determines the evaluation value for each combination pattern passed from the combination pattern acquisition unit 14. The determination unit 16 determines the evaluation value based on the number of samples satisfying the combination pattern among the samples shown in the sample set data and the ratio of the samples satisfying the combination pattern whose label information shows a predetermined value. To do.
  • the number of samples satisfying the combination pattern is the number of samples having a combination of attribute values indicated by the combination pattern, and represents the size of the set satisfying the combination pattern.
  • a sample in which the label information shows a predetermined value is a successful example. That is, the ratio of the samples satisfying the combination pattern whose label information shows a predetermined value is the success rate in the set satisfying the combination pattern.
  • the determination unit 16 sets an evaluation value having a property that the higher the success rate is, the larger the group is the same size, and the larger the group is, the larger the success rate is, for each combination pattern. decide.
  • the evaluation value for example, a chi-square value (X 2 ) can be used.
  • the determination unit 16 passes the evaluation value determined for each combination pattern to the extraction unit 18.
  • the extraction unit 18 extracts a combination pattern having a large evaluation value passed from the determination unit 16 as a segment, which is selected so that the samples included in the set satisfying the combination pattern do not have a large overlap.
  • the extraction unit 18 extracts the combination pattern corresponding to the evaluation value that is the maximum value in the evaluation value for each of the plurality of combination patterns. More specifically, as shown in FIG. 3, the extraction unit 18 refers to a combination pattern in which the evaluation value for a specific combination pattern is one or more data item values added to the specific combination pattern among the plurality of combination patterns. Get the evaluation value. In addition, the extraction unit 18 acquires the evaluation value for the combination pattern in which one or more data item values are deleted from the specific combination pattern. When the evaluation value of the specific combination pattern is higher than any of the acquired evaluation values, the extraction unit 18 extracts the specific combination pattern as a combination pattern having the maximum evaluation value.
  • the extraction unit 18 determines the evaluation value determined by the determination unit 16 when one or more attribute values are added or one or more attribute values are deleted from the combination patterns acquired by the combination pattern acquisition unit 14. Extract combination patterns that decrease. The extraction unit 18 outputs the extracted combination pattern as a segment.
  • the success rate of the set for each of the various combination patterns of the attribute values is obtained by the association analysis, and the set with the success rate set by the threshold value (40% in the example of FIG. 4) or more.
  • the combination pattern corresponding to is extracted as a segment.
  • the samples overlap (the part of the broken line C), and the sets with similar combination patterns are extracted, resulting in an extraction result with poor coverage for the whole. Will end up.
  • the evaluation value as in the present embodiment by using the evaluation value as in the present embodiment and extracting the set that satisfies the combination pattern in which the evaluation value becomes the maximum value, the combination pattern corresponding to the set having a high success rate and a large number of samples is extracted. be able to. Moreover, since the maximum values are rarely adjacent to each other, it is possible to improve the comprehensiveness of the whole when extracting a plurality of segments.
  • an appropriate segment is not always extracted because the extraction result depends on the threshold value setting. For example, as shown in FIG. 4, when the threshold value of the success rate is 40%, the set D and the set E are extracted. If the success rate of the set D and the set F including the set E is less than 40%, even if the set F is an appropriate set from the marketing point of view, it is not extracted as a segment.
  • an appropriate segment does not depend on the success rate threshold value. Can be extracted.
  • the pattern extraction device 10 can be realized by, for example, the computer 40 shown in FIG.
  • the computer 40 includes a CPU (Central Processing Unit) 41, a memory 42 as a temporary storage area, and a non-volatile storage unit 43. Further, the computer 40 includes an input / output device 44 such as an input unit and a display unit, and an R / W (Read / Write) unit 45 that controls reading and writing of data to the storage medium 49. Further, the computer 40 includes a communication I / F (Interface) 46 connected to a network such as the Internet.
  • the CPU 41, the memory 42, the storage unit 43, the input / output device 44, the R / W unit 45, and the communication I / F 46 are connected to each other via the bus 47.
  • the storage unit 43 can be realized by an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like.
  • a pattern extraction program 50 for causing the computer 40 to function as the pattern extraction device 10 is stored in the storage unit 43 as a storage medium.
  • the pattern extraction program 50 includes a sample acquisition process 52, a combination pattern acquisition process 54, a determination process 56, and an extraction process 58.
  • the CPU 41 reads the pattern extraction program 50 from the storage unit 43, expands the pattern extraction program 50 into the memory 42, and sequentially executes the processes included in the pattern extraction program 50.
  • the CPU 41 operates as the sample acquisition unit 12 shown in FIG. 1 by executing the sample acquisition process 52. Further, the CPU 41 operates as the combination pattern acquisition unit 14 shown in FIG. 1 by executing the combination pattern acquisition process 54. Further, the CPU 41 operates as the determination unit 16 shown in FIG. 1 by executing the determination process 56. Further, the CPU 41 operates as the extraction unit 18 shown in FIG. 1 by executing the extraction process 58.
  • the computer 40 that executes the pattern extraction program 50 functions as the pattern extraction device 10.
  • the CPU 41 that executes the program is hardware.
  • the function realized by the pattern extraction program 50 can also be realized by, for example, a semiconductor integrated circuit, more specifically, an ASIC (Application Specific Integrated Circuit) or the like.
  • a semiconductor integrated circuit more specifically, an ASIC (Application Specific Integrated Circuit) or the like.
  • the sample set data input to the pattern extraction device 10 is acquired by the sample acquisition unit 12 and passed to the combination pattern acquisition unit 14. Then, the pattern extraction device 10 executes the pattern extraction process shown in FIG.
  • the pattern extraction process is an example of the pattern extraction method of the disclosed technology.
  • step S12 the combination pattern acquisition unit 14 selects an unselected attribute value from the initial attribute value set, acquires the combination pattern P composed of the selected attribute values, and passes the acquired combination pattern P to the determination unit 16. .. Then, the determination unit 16 identifies a set of samples having the attribute values indicated by the passed combination pattern P, and determines the evaluation value based on the number of samples and the success rate of the set.
  • step S14 the combination pattern acquisition unit 14 acquires a combination pattern in which one attribute value of another attribute is added to the current combination pattern P, and passes it to the determination unit 16. Then, the determination unit 16 determines the evaluation value of the passed combination pattern, and passes the determined evaluation value to the extraction unit 18. Then, the extraction unit 18 searches for an attribute value whose evaluation value increases by adding it by determining whether or not the determined evaluation value is higher than the evaluation value for the combination pattern P.
  • step S16 the extraction unit 18 determines in step S14 whether or not an attribute value whose evaluation value is increased by adding is found. If found, the process proceeds to step S18, and if not found, the process proceeds to step S20.
  • step S18 the combination pattern acquisition unit 14 acquires the combination pattern in which the evaluation value is increased by adding the attribute value added to the current combination pattern P as a new combination pattern P, and passes it to the determination unit 16. Then, the determination unit 16 determines the evaluation value of the passed combination pattern P, and the process returns to step S14.
  • step S20 the combination pattern acquisition unit 14 acquires a combination pattern in which one attribute value is deleted from the current combination pattern P and hands it over to the determination unit 16. Then, the determination unit 16 determines the evaluation value of the passed combination pattern, and passes the determined evaluation value to the extraction unit 18. Then, the extraction unit 18 searches for an attribute value whose evaluation value increases by deleting it by determining whether or not the determined evaluation value is higher than the evaluation value for the combination pattern P.
  • step S22 the extraction unit 18 determines in step S20 whether or not an attribute value whose evaluation value is increased by deleting is found. If found, the process proceeds to step S24, and if not found, the process proceeds to step S26.
  • step S24 the extraction unit 18 deletes the attribute value whose evaluation value increases due to the deletion from the current combination pattern P, acquires a new combination pattern P, and determines the acquired combination pattern P in the determination unit 16. Hand over to. Then, the determination unit 16 determines the evaluation value of the passed combination pattern P, and the process returns to step S14.
  • X 2 of the combination pattern [male ⁇ married] you remove the attribute value "30s” is 11.86
  • X 2 of the combination pattern that you remove the attribute value "male” [30s ⁇ married] at 10.00 is there.
  • step S26 the extraction unit 18 extracts the current combination pattern P as a segment and outputs it.
  • step S28 the extraction unit 18 determines whether or not the extracted segments have reached the specified number. If it has not been reached, the process returns to step S12.
  • the pattern extraction device calculates an evaluation value based on the number of samples of the set satisfying the combination pattern and the success rate for each combination pattern of the attribute values, and the evaluation value is set to the maximum value.
  • the combination pattern is extracted as a segment.
  • the pattern extraction device 210 includes a sample acquisition unit 12, a combination pattern acquisition unit 214, a determination unit 216, and an extraction unit 218.
  • the combination pattern acquisition unit 214 comprehensively acquires a combination pattern of one or more attribute values selected from a plurality of attribute values of each sample included in the sample set data.
  • the combination pattern acquisition unit 214 passes the acquired combination pattern of the attribute values to the determination unit 216.
  • the determination unit 216 determines the same evaluation value as in the first embodiment for each combination pattern delivered from the combination pattern acquisition unit 214. Further, when a predetermined successful example is excluded from the sample set by the extraction unit 218 described later, the determination unit 216 redetermines the evaluation value of each combination pattern for the sample set after exclusion.
  • the extraction unit 218 extracts the combination pattern having the maximum evaluation value determined by the determination unit 216 from the plurality of combination patterns acquired by the combination pattern acquisition unit 214 as a segment. Further, as shown in FIG. 9, the extraction unit 218 excludes the sample of the successful example from the samples satisfying the combination pattern having the maximum evaluation value from the sample set data, and informs the determination unit 216 of the information of the excluded sample. Notice. As a result, the evaluation value is redetermined in the determination unit 216. When the evaluation value is redetermined by the determination unit 216, the extraction unit 218 repeats extracting the combination pattern having the maximum evaluation value based on the redetermined evaluation value, as shown in FIG.
  • the combination pattern with the maximum evaluation value is extracted next, so that the combination pattern with the maximum evaluation value is actually extracted. You will be doing.
  • the pattern extraction device 210 can be realized by, for example, the computer 40 shown in FIG.
  • the storage unit 43 of the computer 40 stores a pattern extraction program 250 for causing the computer 40 to function as the pattern extraction device 210.
  • the pattern extraction program 250 includes a sample acquisition process 52, a combination pattern acquisition process 254, a determination process 256, and an extraction process 258.
  • the CPU 41 reads the pattern extraction program 250 from the storage unit 43, expands the pattern extraction program 250 into the memory 42, and sequentially executes the processes included in the pattern extraction program 250.
  • the CPU 41 operates as the sample acquisition unit 12 shown in FIG. 1 by executing the sample acquisition process 52. Further, the CPU 41 operates as the combination pattern acquisition unit 214 shown in FIG. 1 by executing the combination pattern acquisition process 254. Further, the CPU 41 operates as the determination unit 216 shown in FIG. 1 by executing the determination process 256. Further, the CPU 41 operates as the extraction unit 218 shown in FIG. 1 by executing the extraction process 258.
  • the computer 40 that has executed the pattern extraction program 250 functions as the pattern extraction device 210.
  • the CPU 41 that executes the program is hardware.
  • the function realized by the pattern extraction program 250 can also be realized by, for example, a semiconductor integrated circuit, more specifically, an ASIC or the like.
  • the sample set data input to the pattern extraction device 210 is acquired by the sample acquisition unit 12 and passed to the combination pattern acquisition unit 214. Then, the pattern extraction device 210 executes the pattern extraction process shown in FIG.
  • the pattern extraction process is an example of the pattern extraction method of the disclosed technology.
  • step S212 the combination pattern acquisition unit 214 comprehensively acquires the combination pattern of one or more attribute values selected from the plurality of attribute values of each sample included in the sample set data, and passes it to the determination unit 216. Then, the determination unit 216 determines the evaluation value for each combination pattern delivered from the combination pattern acquisition unit 214, and delivers the evaluation value to the extraction unit 218. Then, the extraction unit 218 extracts the combination pattern having the maximum evaluation value determined by the determination unit 216.
  • step S214 the extraction unit 218 outputs the extracted combination pattern as a segment.
  • step S220 the extraction unit 218 excludes successful sample samples from the sample set data that satisfy the combination pattern (extracted segment) in which the evaluation value extracted in step S212 has the maximum value.
  • step S222 the extraction unit 218 determines whether or not a sample of a successful example remains in the sample set data. If it remains, the extraction unit 218 notifies the determination unit 216 of the information of the sample excluded in step S220, the process returns to step S212, and if it does not remain, the pattern extraction process ends.
  • the determination unit 216 redetermines the evaluation value of each combination pattern for the sample set after excluding successful cases, and the extraction unit 218 redetermines the evaluation value of each combination pattern, and the extraction unit 218 maximizes the evaluation value based on the redetermined evaluation value. Repeatedly extracting the combination pattern of.
  • the pattern extraction device calculates the evaluation value based on the number of samples of the set satisfying the combination pattern and the success rate for each combination pattern of the attribute values, and the evaluation value becomes the maximum. Extract the combination pattern as a segment. Then, the sample of the successful example satisfying the extracted combination pattern is excluded, the calculation of the evaluation value for each combination pattern, and the extraction of the combination pattern having the maximum evaluation value are repeated. This substantially corresponds to extracting the combination pattern in which the evaluation value becomes the maximum value as in the first embodiment. Therefore, as in the first embodiment, it is possible to extract a combination pattern of attribute values that can obtain information on segments that are useful from the viewpoint of marketing.
  • the mode in which the pattern extraction program is stored (installed) in the storage unit in advance has been described, but the present invention is not limited to this.
  • the program according to the disclosed technology can also be provided in a form stored in a storage medium such as a CD-ROM, a DVD-ROM, or a USB memory.
  • Pattern extraction device 10
  • Sample acquisition unit 14 214
  • Combination pattern acquisition unit 16 216
  • Decision unit 18 218
  • Extraction unit 40 Computer 41
  • Memory 43 Storage unit 49
  • Storage media 50 250 Pattern extraction program

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PCT/JP2019/033949 2019-08-29 2019-08-29 パターン抽出プログラム、装置、及び方法 Ceased WO2021038801A1 (ja)

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Application Number Priority Date Filing Date Title
EP19943242.8A EP4024312A4 (en) 2019-08-29 2019-08-29 SAMPLE EXTRACTION PROGRAM, DEVICE AND METHOD
JP2021541906A JP7168095B2 (ja) 2019-08-29 2019-08-29 パターン抽出プログラム、装置、及び方法
PCT/JP2019/033949 WO2021038801A1 (ja) 2019-08-29 2019-08-29 パターン抽出プログラム、装置、及び方法
CN201980099698.8A CN114287017A (zh) 2019-08-29 2019-08-29 模式提取程序、装置以及方法
US17/671,471 US20220172235A1 (en) 2019-08-29 2022-02-14 Storage medium, pattern extraction device, and pattern extraction method

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Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12265521B1 (en) * 2023-11-29 2025-04-01 Stripe, Inc. Methods and systems for authorization rate gradual degradation detection

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009223809A (ja) * 2008-03-18 2009-10-01 Fujitsu Ltd 仕訳条件作成プログラム、仕訳条件作成装置および仕訳条件作成方法
JP2014048780A (ja) * 2012-08-30 2014-03-17 Accenture Global Services Ltd マーケティング装置、マーケティング方法、プログラム、及び記録媒体
JP2016053829A (ja) * 2014-09-03 2016-04-14 ソニー株式会社 情報処理方法、プログラム、及び情報処理装置
JP2016118865A (ja) * 2014-12-19 2016-06-30 インターナショナル・ビジネス・マシーンズ・コーポレーションＩｎｔｅｒｎａｔｉｏｎａｌ Ｂｕｓｉｎｅｓｓ Ｍａｃｈｉｎｅｓ Ｃｏｒｐｏｒａｔｉｏｎ 生成装置、生成方法、及び、プログラム

Family Cites Families (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020049720A1 (en) * 2000-05-11 2002-04-25 Chase Manhattan Bank System and method of data mining
JP3516144B1 (ja) * 2002-06-18 2004-04-05 オムロン株式会社 光学情報コードの読取方法および光学情報コード読取装置
US20050071223A1 (en) * 2003-09-30 2005-03-31 Vivek Jain Method, system and computer program product for dynamic marketing strategy development
US20050216525A1 (en) * 2004-03-26 2005-09-29 Andre Wachholz-Prill Defining target group for marketing campaign
US7627620B2 (en) * 2004-12-16 2009-12-01 Oracle International Corporation Data-centric automatic data mining
US20110258049A1 (en) * 2005-09-14 2011-10-20 Jorey Ramer Integrated Advertising System
US20070112733A1 (en) * 2005-11-14 2007-05-17 Beyer Dirk M Method and system for extracting customer attributes
JP5228461B2 (ja) * 2007-12-05 2013-07-03 富士通株式会社 パターン抽出装置、パターン抽出プログラムおよびパターン抽出方法
JP5512096B2 (ja) * 2008-05-12 2014-06-04 株式会社東芝 情報分析装置およびその制御プログラム
US8554602B1 (en) * 2009-04-16 2013-10-08 Exelate, Inc. System and method for behavioral segment optimization based on data exchange
JP5455978B2 (ja) * 2011-06-08 2014-03-26 株式会社東芝 パターン抽出装置及び方法
JP5871842B2 (ja) * 2013-03-04 2016-03-01 日本電信電話株式会社 情報可視化装置、方法及びプログラム
JP5735034B2 (ja) * 2013-05-09 2015-06-17 日本電信電話株式会社 プロフィール推定モデル学習装置、プロフィール推定装置、方法、及びプログラム
JP2016035684A (ja) * 2014-08-04 2016-03-17 日本電信電話株式会社 情報管理システム、情報管理方法、及び情報管理プログラム
US10178043B1 (en) * 2014-12-08 2019-01-08 Conviva Inc. Dynamic bitrate range selection in the cloud for optimized video streaming
US11138618B1 (en) * 2015-06-22 2021-10-05 Amazon Technologies, Inc. Optimizing in-application purchase items to achieve a developer-specified metric
US11188940B1 (en) * 2015-09-01 2021-11-30 Groupon, Inc. Method, apparatus, and computer program for predicting consumer behavior
CN105389548A (zh) * 2015-10-23 2016-03-09 南京邮电大学 基于人脸识别的婚恋评价系统和方法
CN106651409A (zh) * 2015-10-29 2017-05-10 北京京东尚科信息技术有限公司 预测用户婚姻状态的方法和装置
WO2017094207A1 (ja) * 2015-11-30 2017-06-08 日本電気株式会社 情報処理システム、情報処理方法および情報処理用プログラム
CN105701498B (zh) * 2015-12-31 2021-09-07 腾讯科技（深圳）有限公司 一种用户分类方法及服务器
US20170193538A1 (en) * 2016-01-06 2017-07-06 Oracle International Corporation System and method for determining the priority of mixed-type attributes for customer segmentation
CA3015447C (en) * 2016-02-22 2021-09-14 Tata Consultancy Services Limited Systems and methods for computing data privacy-utility tradeoff
US10936620B2 (en) * 2016-11-22 2021-03-02 Flytxt BV Systems and methods for management of multi-perspective customer segments
CN106971317A (zh) * 2017-03-09 2017-07-21 杨伊迪 基于人脸识别与大数据分析的广告投放效果评价与智能推送决策方法
CN106919706A (zh) * 2017-03-10 2017-07-04 广州视源电子科技股份有限公司 数据更新的方法及装置
WO2018207259A1 (ja) * 2017-05-09 2018-11-15 日本電気株式会社 情報処理システム、情報処理装置、予測モデル抽出方法および予測モデル抽出プログラム
US20180361253A1 (en) * 2017-05-22 2018-12-20 Scientific Revenue, Inc. Method of automating segmentation of users of game or online service with limited a priori knowledge
JP6828830B2 (ja) * 2017-11-02 2021-02-10 日本電気株式会社 評価システム、評価方法および評価用プログラム
US10769647B1 (en) * 2017-12-21 2020-09-08 Wells Fargo Bank, N.A. Divergent trend detection and mitigation computing system
CN109147949A (zh) * 2018-08-16 2019-01-04 辽宁大学 一种基于分类回归树来用于检测教师亚健康状态的方法
US10248527B1 (en) * 2018-09-19 2019-04-02 Amplero, Inc Automated device-specific dynamic operation modifications
CN109949175B (zh) * 2019-03-26 2023-05-05 桂林电子科技大学 一种基于协同过滤和相似性度量的用户属性推断方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009223809A (ja) * 2008-03-18 2009-10-01 Fujitsu Ltd 仕訳条件作成プログラム、仕訳条件作成装置および仕訳条件作成方法
JP2014048780A (ja) * 2012-08-30 2014-03-17 Accenture Global Services Ltd マーケティング装置、マーケティング方法、プログラム、及び記録媒体
JP2016053829A (ja) * 2014-09-03 2016-04-14 ソニー株式会社 情報処理方法、プログラム、及び情報処理装置
JP2016118865A (ja) * 2014-12-19 2016-06-30 インターナショナル・ビジネス・マシーンズ・コーポレーションＩｎｔｅｒｎａｔｉｏｎａｌ Ｂｕｓｉｎｅｓｓ Ｍａｃｈｉｎｅｓ Ｃｏｒｐｏｒａｔｉｏｎ 生成装置、生成方法、及び、プログラム

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4024312A4 *

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