公开(公告)号：US20110231350A1

公开(公告)日：2011-09-22

申请号：US13130950

申请日：2009-11-24

申请人： Michinari Momma ,  Satoshi Morinaga ,  Daisuke Komura

发明人： Michinari Momma ,  Satoshi Morinaga ,  Daisuke Komura

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F17/30943 ,  G06K9/6218

摘要： An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes.

摘要翻译： 活动度量学习装置包括度量应用数据分析单元，度量优化单元和属性聚类单元。 度量应用数据分析单元形成有用于计算要分析的数据之间的距离的度量应用模块，用于使用预定函数分析数据的数据分析模块和要分析的数据之间的距离，并输出 数据分析以及用于存储数据分析结果的分析结果存储单元。 度量优化单元由反馈转换模块形成，用于根据来自用户的反馈命令创建侧信息，以及度量学习模块，用于使用创建的侧信息生成在预定条件下优化的度量矩阵。 属性聚类单元聚合由度量优化单元优化的度量矩阵，并对属性进行结构化。

公开(公告)号：US08650138B2

公开(公告)日：2014-02-11

申请号：US13130950

申请日：2009-11-24

申请人： Michinari Momma ,  Satoshi Morinaga ,  Daisuke Komura

发明人： Michinari Momma ,  Satoshi Morinaga ,  Daisuke Komura

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F17/30943 ,  G06K9/6218

摘要： An active metric learning device includes a metric application data analysis unit, a metric optimization unit, and an attribute clustering unit. The metric application data analysis unit is formed with a metric applying module for calculating the distance between data to be analyzed, a data analyzing module for analyzing the data using a predetermined function and the distances between the data to be analyzed and outputting the result of the data analysis, and an analysis result storage unit for storing the result of the data analysis. The metric optimization unit is formed with a feedback converting module for creating side information according to the command of feedback from the user and a metric learning module for generating a metric matrix optimized under a predetermined condition using the created side information. The attribute clustering unit clusters the metric matrix optimized by the metric optimization unit and structuralizes the attributes.

摘要翻译： 活动度量学习装置包括度量应用数据分析单元，度量优化单元和属性聚类单元。 度量应用数据分析单元形成有用于计算要分析的数据之间的距离的度量应用模块，用于使用预定函数分析数据的数据分析模块和要分析的数据之间的距离，并输出 数据分析以及用于存储数据分析结果的分析结果存储单元。 度量优化单元由反馈转换模块形成，用于根据来自用户的反馈命令创建侧信息，以及度量学习模块，用于使用创建的侧信息生成在预定条件下优化的度量矩阵。 属性聚类单元聚合由度量优化单元优化的度量矩阵，并对属性进行结构化。

公开(公告)号：US20130013536A1

公开(公告)日：2013-01-10

申请号：US13519076

申请日：2010-12-24

申请人： Michinari Momma ,  Satoshi Morinaga

发明人： Michinari Momma ,  Satoshi Morinaga

IPC分类号： G06F15/18

CPC分类号： G06F16/90

摘要： A metric learning device (110) is provided with: a storage unit (800) which stores data to be analyzed having a plurality of attributes, feedback information from a user, and metric information; a feedback converting unit (200) which converts the data to be analyzed into side information on the basis of the attribute of the data to be analyzed and/or the feedback information; a metric learning unit (300) which optimizes the metric information on the basis of the side information; a data analysis unit (400) which analyzes the data to be analyzed on the basis of the optimized metric information, and which outputs the analysis results thereof; and a client control unit (700) which displays the analysis results on a plurality of client devices, and which receives, from the plurality of client devices, feedback information which were received in response to the analysis results.

摘要翻译： 度量学习装置（110）具有：存储单元（800），存储具有多个属性的分析数据，来自用户的反馈信息和度量信息; 反馈转换单元，其根据要分析的数据的属性和/或反馈信息将要分析的数据转换为侧信息; 度量学习单元（300），其基于所述侧面信息优化所述度量信息; 数据分析单元，其基于优化的度量信息分析要分析的数据，并输出其分析结果; 以及客户控制单元，其将分析结果显示在多个客户端设备上，并且从多个客户端设备接收响应于分析结果接收的反馈信息。

公开(公告)号：US20110004578A1

公开(公告)日：2011-01-06

申请号：US12918832

申请日：2008-12-08

申请人： Michinari Momma ,  Satoshi Morinaga ,  Norikazu Matsumura ,  Daisuke Komura

发明人： Michinari Momma ,  Satoshi Morinaga ,  Norikazu Matsumura ,  Daisuke Komura

IPC分类号： G06F15/18

CPC分类号： G06N20/00

摘要： A metric application unit receives data under analysis having a plurality of attributes and a metric indicative of the distance between the data under analysis, calculates the distance between the data under analysis, and output and stores a data analysis result which is generated from an analysis on the data under analysis with a predetermined function, using the calculated distance between the data under analysis. A metric optimization unit generates side-information based on an indication of feedback information entered from the outside and including either similarities between the data under analysis, or the attributes, or a combination thereof, generates a metric which complies with a predetermined condition, based on the generated side information, and stores the generated metric in a metric learning result storage unit.

摘要翻译： 度量应用单元接收具有多个属性的分析数据，以及表示被分析数据之间的距离的度量，计算被分析数据之间的距离并输出，并存储从分析生成的数据分析结果 用预定功能分析的数据，使用计算出的数据分析距离。 度量优化单元基于从外部输入的反馈信息的指示并且包括被分析数据之间的相似性或属性或其组合，基于预定条件生成符合预定条件的度量来产生侧信息，基于 生成的侧信息，并将生成的度量存储在度量学习结果存储单元中。

公开(公告)号：US20100324985A1

公开(公告)日：2010-12-23

申请号：US12857317

申请日：2010-08-16

申请人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

发明人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

IPC分类号： G06Q30/00 ,  G06N5/02

CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体（例如产品和客户）之间的关系中的模式，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。

公开(公告)号：US20100318334A1

公开(公告)日：2010-12-16

申请号：US12866828

申请日：2009-02-09

申请人： Michinari Momma

发明人： Michinari Momma

IPC分类号： G06G7/48

CPC分类号： G06K9/6269

摘要： The data analysis apparatus (100) of the present invention includes a control unit (180) that, upon input of a plurality of data that are the object of analysis, sets constraints that take as version space a space that is enclosed by planes that contain these data and moreover that are perpendicular to each of the plurality of data in model parameter space, maximizes the size of a shape that is inscribed in a plurality of planes that enclose the version space, and finds the center of the shape.

摘要翻译： 本发明的数据分析装置（100）包括：控制单元（180），当输入作为分析对象的多个数据时，设定作为版本空间的约束，该空间包含由 这些数据，而且在模型参数空间中垂直于多个数据中的每一个，使包围该版本空间的多个平面中内接的形状的大小最大化，并且找到该形状的中心。

公开(公告)号：US07801843B2

公开(公告)日：2010-09-21

申请号：US11327822

申请日：2006-01-06

申请人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

发明人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

IPC分类号： G06F15/18 ,  G06F17/00 ,  G06N5/04

CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体之间的关系中的模式，例如产品和客户，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。

公开(公告)号：US20070100680A1

公开(公告)日：2007-05-03

申请号：US11256386

申请日：2005-10-21

申请人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

发明人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

IPC分类号： G06F17/30

CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体（例如产品和客户）之间的关系中的模式，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。

公开(公告)号：US08015140B2

公开(公告)日：2011-09-06

申请号：US12857317

申请日：2010-08-16

申请人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

发明人： Shailesh Kumar ,  Edmond D. Chow ,  Michinari Momma

IPC分类号： G06F17/00 ,  G06N5/02

CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.

摘要翻译： 本发明在本文中称为PeaCoCk，使用来自统计学，信息理论和图形理论的独特技术融合来量化和发现实体（例如产品和客户）之间的关系中的模式，如购买行为所证明的。 与传统的基于购买频率的市场篮子分析技术（如大多数产生明显和虚假关联的关联规则）相反，PeaCoCk采用信息理论的一致性和相似性概念，这使得对真实的，统计上显着的和合乎逻辑的逻辑分析 产品之间的关联。 因此，PeaCoCk可以根据购买行为进行可靠，强大的预测分析。

公开(公告)号：US20070094067A1

公开(公告)日：2007-04-26

申请号：US11355567

申请日：2006-02-15

申请人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

发明人： Shailesh Kumar ,  Edmond Chow ,  Michinari Momma

IPC分类号： G06F17/30

CPC分类号： G06Q30/06 ,  G06Q10/04 ,  G06Q10/06 ,  G06Q30/02 ,  G06Q30/0201 ,  G06Q30/0202 ,  G06Q30/0204 ,  G06Q30/0224 ,  G06Q30/0605

摘要： The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.