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11. 发明授权

US07379939B2 Methods for dynamic classification of data in evolving data stream 失效
标题翻译：在进化数据流中数据的动态分类方法
公开(公告)号：US07379939B2
公开(公告)日：2008-05-27
申请号：US10881036
申请日：2004-06-30
申请人： Charu C. Aggarwal , Philip Shi-Lung Yu
发明人： Charu C. Aggarwal , Philip Shi-Lung Yu
IPC分类号： G06F7/00
CPC分类号： G06F17/30516 , G06F17/30598 , Y10S707/99942 , Y10S707/99943
摘要： A technique for classifying data from a test data stream is provided. A stream of training data having class labels is received. One or more class-specific clusters of the training data are determined and stored. At least one test instance of the test data stream is classified using the one or more class-specific clusters.
摘要翻译：提供了一种从测试数据流中分类数据的技术。接收具有类标签的训练数据流。确定并存储训练数据的一个或多个类特定的簇。测试数据流的至少一个测试实例使用一个或多个类特定簇进行分类。

12. 发明申请

US20080082475A1 SYSTEM AND METHOD FOR RESOURCE ADAPTIVE CLASSIFICATION OF DATA STREAMS 失效
标题翻译：资源自适应分类数据流系统与方法
公开(公告)号：US20080082475A1
公开(公告)日：2008-04-03
申请号：US11530938
申请日：2006-09-12
申请人： Charu C. Aggarwal , Philip Shi-lung Yu
发明人： Charu C. Aggarwal , Philip Shi-lung Yu
IPC分类号： G06N5/02
CPC分类号： G06K9/6282 , G06N99/005
摘要： A system and method for resource adaptive classification of data streams. Embodiments of systems and methods provide classifying data received in a computer, including discretizing the received data, constructing an intermediate data structure from said received data as training instances, performing subspace sampling on said received data as test instances and adaptively classifying said received data based on statistics of said subspace sampling.
摘要翻译：一种用于数据流资源自适应分类的系统和方法。系统和方法的实施例提供在计算机中接收的分类数据，包括离散接收的数据，从所接收的数据构建中间数据结构作为训练实例，对所接收的数据进行子空间采样作为测试实例，并基于所述子空间抽样统计。

13. 发明授权

US07236638B2 Methods and apparatus for reduction of high dimensional data 有权
标题翻译：减少高维数据的方法和装置
公开(公告)号：US07236638B2
公开(公告)日：2007-06-26
申请号：US10208523
申请日：2002-07-30
申请人： Charu C. Aggarwal
发明人： Charu C. Aggarwal
IPC分类号： G06K9/36 , G06K9/46
CPC分类号： G06K9/6232 , Y10S707/99931
摘要： Data compression techniques particularly applicable to high dimensional data. The invention uses a hierarchical partitioning approach in conjunction with a subspace sampling methodology which is sensitive to a subject data set. The dual nature of this hierarchical partitioning and subspace sampling approach makes the overall data compression process very effective. While the data compression process provides a much more compact representation than traditional dimensionality reduction techniques, the process also provides hard bounds on the error of the approximation. Also, the data compression process of the invention realizes a compression factor that improves with increasing database size.
摘要翻译：数据压缩技术特别适用于高维数据。本发明结合对主题数据集敏感的子空间抽样方法使用分层划分方法。这种分层分区和子空间采样方法的双重性使得整体数据压缩过程非常有效。虽然数据压缩过程提供比传统降维技术更紧凑的表示，但是该过程也提供了对近似误差的硬限制。此外，本发明的数据压缩处理实现了随着数据库大小的增加而改善的压缩因子。

14. 发明授权

US06408295B1 System and method of using clustering to find personalized associations 失效
标题翻译：使用群集查找个性化关联的系统和方法
公开(公告)号：US06408295B1
公开(公告)日：2002-06-18
申请号：US09334764
申请日：1999-06-16
申请人： Charu C. Aggarwal , Philip Shi-lung Yu
发明人： Charu C. Aggarwal , Philip Shi-lung Yu
IPC分类号： G06F1730
CPC分类号： G06F17/30705 , Y10S707/99936
摘要： A system and method for developing association rules which are personalized for a customer. The method includes partitioning (clustering) a set of records corresponding to transactions of items into discrete segments so that different parts of the data show different kinds of trends. The clustering is used in order to create a segmentation of the data such that these trends are captured in each segment. Consequently, a different set of association rules are relevant for each segment. For a given customer, the segment to which he/she belongs most closely may be readily determined, and the trends in that segment may be used for generating the association rules.
摘要翻译：用于开发针对客户个性化的关联规则的系统和方法。该方法包括将对应于项目的事务的一组记录分组（聚集）成离散的段，使得数据的不同部分显示出不同种类的趋势。使用聚类来创建数据的分割，使得这些趋势在每个段中被捕获。因此，不同的关联规则集合对于每个段是相关的。对于给定的客户，他/她属于最密切的部分可以容易地确定，并且该部分的趋势可以用于生成关联规则。

15. 发明授权

US06356879B2 Content based method for product-peer filtering 有权
标题翻译：基于内容的产品 - 对等过滤方法
公开(公告)号：US06356879B2
公开(公告)日：2002-03-12
申请号：US09169029
申请日：1998-10-09
申请人： Charu C. Aggarwal , Philip S. Yu
发明人： Charu C. Aggarwal , Philip S. Yu
IPC分类号： G06F1760
CPC分类号： G06Q30/02 , G06Q30/0253 , G06Q30/0625 , G06Q30/0631 , Y10S707/99933 , Y10S707/99943
摘要： The present invention derives product characterizations for products offered at an e-commerce site based on the text descriptions of the products provided at the site. A customer characterization is generated for any customer browsing the e-commerce site. The characterizations include an aggregation of derived product characterizations associated with products bought and/or browsed by that customer. A peer group is formed by clustering customers having similar customer characterizations. Recommendations are then made to a customer based on the processed characterization and peer group data.
摘要翻译：本发明基于在现场提供的产品的文本描述，得出在电子商务站点提供的产品的产品特性。为浏览电子商务网站的任何客户生成客户表征。表征包括与由该客户购买和/或浏览的产品相关联的衍生产品表征的聚合。通过对具有类似客户特征的客户进行聚类形成对等组。然后根据经处理的特征和对等体组数据向客户提供建议。

16. 发明授权

US08659604B2 Dimensional reduction mechanisms for representing massive communication network graphs for structural queries 失效
标题翻译：用于表示结构性查询的大量通信网络图的尺寸缩减机制
公开(公告)号：US08659604B2
公开(公告)日：2014-02-25
申请号：US12568719
申请日：2009-09-29
申请人： Charu C. Aggarwal
发明人： Charu C. Aggarwal
IPC分类号： G06T11/20
CPC分类号： G06F17/30572 , G06T11/206
摘要： Mechanisms are provided for transforming an original graph data set into a representative form having a smaller number of dimensions that the original graph data set. The mechanisms generate a graph transformation basis structure based on an input graph data structure. The mechanisms further transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure. The transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set. Moreover, the mechanisms perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component.
摘要翻译：提供了用于将原始图形数据集变换为具有较小维数原始图形数据集的代表形式的机制。该机制基于输入图形数据结构生成图形变换基础结构。这些机制基于图形变换基础结构和输入图形数据结构的交点进一步变换原始图形数据集，从而生成变换图形数据集数据结构。变换后的图形数据集数据结构具有与输入图形数据结构的维度相当的维度，但表示原始图形数据集的特征。此外，机构对变换的图形数据集数据结构执行应用程序特定的操作，以在转换的图形数据集中产生最接近的相似度记录的输出到目标分量。

17. 发明授权

US08650213B2 System and method for distributed privacy preserving data mining 失效
标题翻译：分布式隐私保护数据挖掘的系统和方法
公开(公告)号：US08650213B2
公开(公告)日：2014-02-11
申请号：US11752708
申请日：2007-05-23
申请人： Charu C. Aggarwal , Philip Shi-Lung Yu
发明人： Charu C. Aggarwal , Philip Shi-Lung Yu
IPC分类号： G06F7/00
CPC分类号： G06F17/30539 , G06F21/6245 , G06F2216/03 , Y10S707/99931 , Y10S707/99932
摘要： Distributed privacy preserving data mining techniques are provided. A first entity of a plurality of entities in a distributed computing environment exchanges summary information with a second entity of the plurality of entities via a privacy-preserving data sharing protocol such that the privacy of the summary information is preserved, the summary information associated with an entity relating to data stored at the entity. The first entity may then mine data based on at least the summary information obtained from the second entity via the privacy-preserving data sharing protocol. The first entity may obtain, from the second entity via the privacy-preserving data sharing protocol, information relating to the number of transactions in which a particular itemset occurs and/or information relating to the number of transactions in which a particular rule is satisfied.
摘要翻译：提供分布式隐私保护数据挖掘技术。分布式计算环境中的多个实体的第一实体通过隐私保护数据共享协议与多个实体的第二实体交换摘要信息，使得保留摘要信息的隐私，与与实体存储的数据相关的实体。然后，第一实体可以至少基于通过隐私保护数据共享协议从第二实体获得的摘要信息来挖掘数据。第一实体可以通过隐私保护数据共享协议从第二实体获得与特定项目集出现的交易数量有关的信息和/或与其中满足特定规则的交易数量有关的信息。

18. 发明申请

US20120269200A1 Similarity Searching in Large Disk-Based Networks 有权
标题翻译：在大型基于磁盘的网络中进行相似性搜索
公开(公告)号：US20120269200A1
公开(公告)日：2012-10-25
申请号：US13091244
申请日：2011-04-21
申请人： Charu C. Aggarwal
发明人： Charu C. Aggarwal
IPC分类号： H04L12/56
CPC分类号： H04L47/00
摘要： Techniques for determining a shortest path in a disk-based network are provided. The techniques include creating a compressed representation of an underlying disk resident network graph, wherein creating a compressed representation of an underlying disk resident network graph comprises determining one or more dense regions in the disk resident graph and compacting the one or more dense regions into one or more compressed nodes, associating one or more node penalties with the one or more compressed nodes, wherein the one or more node penalties reflect a distance of a sub-path within a compressed node, and performing a query on the underlying disk resident network graph using the compressed representation and one or more node penalties to determine a shortest path in the disk-based network to reduce the number of accesses to a physical disk.
摘要翻译：提供用于确定基于磁盘的网络中的最短路径的技术。所述技术包括创建底层磁盘驻留网络图的压缩表示，其中创建底层磁盘驻留网络图的压缩表示包括确定磁盘驻留图中的一个或多个密集区域并将一个或多个密集区域压缩为一个或多个密集区域更多的压缩节点将一个或多个节点惩罚与一个或多个压缩节点相关联，其中所述一个或多个节点惩罚反映了压缩节点内的子路径的距离，并且使用压缩表示和一个或多个节点惩罚，以确定基于磁盘的网络中的最短路径，以减少对物理磁盘的访问次数。

19. 发明申请

US20120218908A1 System and Method for Finding Important Nodes in a Network 失效
标题翻译：在网络中查找重要节点的系统和方法
公开(公告)号：US20120218908A1
公开(公告)日：2012-08-30
申请号：US13036083
申请日：2011-02-28
申请人： Charu C. Aggarwal
发明人： Charu C. Aggarwal
IPC分类号： H04L12/26
CPC分类号： H04L51/32 , H04L51/14
摘要： Techniques for optimizing steady state flow of a network are provided. The techniques include determining a first set of two or more nodes in a network, computing a steady-state flow probability of the first set of two or more nodes, and iteratively interchanging nodes from a second set of two or more nodes into the first set of two or more nodes to determine an optimum total steady state flow of the network, wherein determining an optimum total steady-state flow of the network comprises iteratively interchanging nodes until no additional improvements in steady-state flow over the computed steady-state flow probability can be obtained.
摘要翻译：提供了优化网络稳态流的技术。这些技术包括确定网络中的两个或更多个节点的第一组，计算第二组两个或多个节点的稳态流概率，以及将节点从第二组两个或多个节点迭代地交换到第一组中以确定网络的最佳总稳态流，其中确定网络的最佳总稳态流包括迭代交换节点，直到在所计算的稳态流概率上没有对稳态流的额外改进可以获得。

20. 发明申请

US20110074786A1 Dimensional Reduction Mechanisms for Representing Massive Communication Network Graphs for Structural Queries 失效
标题翻译：用于表示大量通信网络图的结构查询的维度缩减机制
公开(公告)号：US20110074786A1
公开(公告)日：2011-03-31
申请号：US12568719
申请日：2009-09-29
申请人： Charu C. Aggarwal
发明人： Charu C. Aggarwal
IPC分类号： G06T11/20
CPC分类号： G06F17/30572 , G06T11/206
摘要： Mechanisms are provided for transforming an original graph data set into a representative form having a smaller number of dimensions that the original graph data set. The mechanisms generate a graph transformation basis structure based on an input graph data structure. The mechanisms further transform an original graph data set based on an intersection of the graph transformation basis structure and the input graph data structure to thereby generate a transformed graph data set data structure. The transformed graph data set data structure has a reduced dimensionality from that of the input graph data structure but represents characteristics of the original graph data set. Moreover, the mechanisms perform an application specific operation on the transformed graph data set data structure to generate an output of a closest similarity record in the transformed graph data set to a target component.
摘要翻译：提供了用于将原始图形数据集变换为具有较小维数原始图形数据集的代表形式的机制。该机制基于输入图形数据结构生成图形变换基础结构。这些机制基于图形变换基础结构和输入图形数据结构的交点进一步变换原始图形数据集，从而生成变换图形数据集数据结构。变换后的图形数据集数据结构具有与输入图形数据结构的维度相当的维度，但表示原始图形数据集的特征。此外，机构对变换的图形数据集数据结构执行应用程序特定的操作，以在转换的图形数据集中产生最接近的相似度记录的输出到目标分量。

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