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公开(公告)号：US07743058B2
公开(公告)日：2010-06-22
申请号：US11621848
申请日：2007-01-10
申请人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
发明人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
CPC分类号： G06F17/30705 , G06K9/6226
摘要： A method and system for high-order co-clustering of objects of heterogeneous types is provided. A clustering system co-clusters objects of heterogeneous types based on joint distributions for objects of non-central types and objects of a central type. The clustering system uses an iterative approach to co-clustering the objects of the various types. The clustering system divides the co-clustering into a sub-problem, for each non-central type (e.g., first type and second type), of co-clustering objects of that non-central type and objects of the central type based on the joint distribution for that non-central type. After the co-clustering is completed, the clustering system clusters objects of the central type based on the clusters of the objects of the non-central types identified during co-clustering. The clustering system repeats the iterations until the clusters of objects of the central type converge on a solution.
摘要翻译： 提供了一种用于异构类型对象的高阶共聚集的方法和系统。 基于非中心类型对象和中心类型对象的联合分布，聚类系统将异构类型的对象共同聚集。 聚类系统使用迭代方法来共同分类各种类型的对象。 对于每个非中心类型（例如，第一类型和第二类型），聚类系统将共聚类分为非中心类型的共聚类对象和中心类型的对象的子问题，基于 联合分配为非中央型。 在共同聚集完成之后，聚类系统基于在共聚集期间识别的非中心类型的对象的聚类来聚类中心类型的对象。 聚类系统重复迭代，直到中心类型的对象集合在解上。
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公开(公告)号：US20080168061A1
公开(公告)日：2008-07-10
申请号：US11621848
申请日：2007-01-10
申请人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
发明人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
IPC分类号： G06F17/30
CPC分类号： G06F17/30705 , G06K9/6226
摘要： A method and system for high-order co-clustering of objects of heterogeneous types is provided. A clustering system co-clusters objects of heterogeneous types based on joint distributions for objects of non-central types and objects of a central type. The clustering system uses an iterative approach to co-clustering the objects of the various types. The clustering system divides the co-clustering into a sub-problem, for each non-central type (e.g., first type and second type), of co-clustering objects of that non-central type and objects of the central type based on the joint distribution for that non-central type. After the co-clustering is completed, the clustering system clusters objects of the central type based on the clusters of the objects of the non-central types identified during co-clustering. The clustering system repeats the iterations until the clusters of objects of the central type converge on a solution.
摘要翻译： 提供了一种用于异构类型对象的高阶共聚集的方法和系统。 基于非中心类型对象和中心类型对象的联合分布，聚类系统将异构类型的对象共同聚集。 聚类系统使用迭代方法来共同分类各种类型的对象。 对于每个非中心类型（例如，第一类型和第二类型），聚类系统将共聚类分为非中心类型的共聚类对象和中心类型的对象的子问题，基于 联合分配为非中央型。 在共同聚集完成之后，聚类系统基于在共聚集期间识别的非中心类型的对象的聚类来聚类中心类型的对象。 聚类系统重复迭代，直到中心类型的对象集合在解上。
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公开(公告)号：US07974977B2
公开(公告)日：2011-07-05
申请号：US11743942
申请日：2007-05-03
申请人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
发明人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
CPC分类号： G06K9/6224 , G06F17/3071
摘要： A clustering system generates an original Laplacian matrix representing objects and their relationships. The clustering system initially applies an eigenvalue decomposition solver to the original Laplacian matrix for a number of iterations. The clustering system then identifies the elements of the resultant eigenvector that are stable. The clustering system then aggregates the elements of the original Laplacian matrix corresponding to the identified stable elements and forms a new Laplacian matrix that is a compressed form of the original Laplacian matrix. The clustering system repeats the applying of the eigenvalue decomposition solver and the generating of new compressed Laplacian matrices until the new Laplacian matrix is small enough so that a final solution can be generated in a reasonable amount of time.
摘要翻译： 聚类系统生成表示对象及其关系的原始拉普拉斯矩阵。 聚类系统首先将特征值分解求解器应用于原始拉普拉斯矩阵进行多次迭代。 然后，聚类系统识别所得到的特征向量的元素是稳定的。 然后，聚类系统聚合对应于所识别的稳定元素的原始拉普拉斯矩阵的元素，并形成作为原始拉普拉斯矩阵的压缩形式的新的拉普拉斯矩阵。 聚类系统重复应用特征值分解求解器和生成新的压缩拉普拉斯矩阵，直到新的拉普拉斯矩阵足够小，以便在合理的时间内生成最终解。
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公开(公告)号：US07461073B2
公开(公告)日：2008-12-02
申请号：US11354208
申请日：2006-02-14
申请人： Bin Gao , Tie-Yan Liu , Wei-Ying Ma
发明人： Bin Gao , Tie-Yan Liu , Wei-Ying Ma
CPC分类号： G06F17/30598 , G06K9/6224 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： A method and system for high-order co-clustering of objects of heterogeneous types using multiple bipartite graphs is provided. A clustering system represents relationships between objects of a first type and objects of a third type as a first bipartite graph and relationships between objects of a second type and objects of the third type as a second bipartite graph. The clustering system defines an objective function that specifies an objective of the clustering process that combines an objective for the first bipartite graph and an objective for the second bipartite graph. The clustering system solves the objective function and then applies a clustering algorithm such as the K-means algorithm to the solution to identify the clusters of heterogeneous objects.
摘要翻译： 提供了使用多个二分图的异构类型的对象的高阶共聚的方法和系统。 聚类系统表示第一类型的对象与第三类型的对象之间的关系，作为第二个二分图，第二类的对象与第三类的对象之间的关系作为第二个二分图。 聚类系统定义了一个目标函数，该目标函数指定了组合第一个二分图的目标和第二个二分图的目标的聚类过程的目标。 聚类系统解决了目标函数，然后将K-means算法的聚类算法应用于解决方案，以识别异构对象的簇。
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公开(公告)号：US20070192350A1
公开(公告)日：2007-08-16
申请号：US11354208
申请日：2006-02-14
申请人： Bin Gao , Tie-Yan Liu , Wei-Ying Ma
发明人： Bin Gao , Tie-Yan Liu , Wei-Ying Ma
IPC分类号： G06F7/00
CPC分类号： G06F17/30598 , G06K9/6224 , Y10S707/99942 , Y10S707/99943 , Y10S707/99945
摘要： A method and system for high-order co-clustering of objects of heterogeneous types using multiple bipartite graphs is provided. A clustering system represents relationships between objects of a first type and objects of a third type as a first bipartite graph and relationships between objects of a second type and objects of the third type as a second bipartite graph. The clustering system defines an objective function that specifies an objective of the clustering process that combines an objective for the first bipartite graph and an objective for the second bipartite graph. The clustering system solves the objective function and then applies a clustering algorithm such as the K-means algorithm to the solution to identify the clusters of heterogeneous objects.
摘要翻译： 提供了使用多个二分图的异构类型的对象的高阶共聚的方法和系统。 聚类系统表示第一类型的对象与第三类型的对象之间的关系，作为第二个二分图，第二类的对象与第三类的对象之间的关系作为第二个二分图。 聚类系统定义了一个目标函数，该目标函数指定了组合第一个二分图的目标和第二个二分图的目标的聚类过程的目标。 聚类系统解决了目标函数，然后将K-means算法的聚类算法应用于解决方案，以识别异构对象的簇。
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公开(公告)号：US20120253945A1
公开(公告)日：2012-10-04
申请号：US13078454
申请日：2011-04-01
申请人： Bin Gao , Tie-Yan Liu , Tao Qin , Zeyong Xu , Jianhua Hu , Wei-Ying Ma
发明人： Bin Gao , Tie-Yan Liu , Tao Qin , Zeyong Xu , Jianhua Hu , Wei-Ying Ma
CPC分类号： G06Q30/0275
摘要： Some implementations provide techniques for estimating impression numbers. For example, a log of advertisement bidding data may be used to generate and train an impression estimation model. In some implementations, an impression estimation component may use a boost regression technique to determine a predicted impression value range based on a proposed bid received from an advertiser. For example, the predicted impression value range may be determined based on a predicted estimation error. Additionally, in some instances, the predicted impression value range may be evaluated using one or more evaluation metrics.
摘要翻译： 一些实现提供了用于估计印象数的技术。 例如，可以使用广告投标数据的日志来生成和训练印象估计模型。 在一些实现中，印象估计组件可以使用增强回归技术来基于从广告商接收到的提议的出价来确定预测的印象值范围。 例如，可以基于预测的估计误差来确定预测的印象值范围。 另外，在某些情况下，可以使用一个或多个评估度量来评估预测的印象值范围。
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公开(公告)号：US20120253927A1
公开(公告)日：2012-10-04
申请号：US13078598
申请日：2011-04-01
申请人： Tao Qin , Tie-Yan Liu , Bin Gao , Jingyi Xu , Zeyong Xu , Wei-Ying Ma
发明人： Tao Qin , Tie-Yan Liu , Bin Gao , Jingyi Xu , Zeyong Xu , Wei-Ying Ma
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0241
摘要： Some implementations generate a mapping function using one or more historic performance indicators for a set of ad-keyword pairs and one or more advertisement metrics extracted from the set of ad-keyword pairs. The mapping function may be applied to map one or more advertisement metrics of a particular ad-keyword pair to determine a quality score for the particular ad-keyword pair. For example, the quality score may be used when determining whether to select an advertisement for display or may be provided as feedback to an advertiser. Additionally, in some implementations, the mapping function may be applied to determine a quality score for a new ad-keyword pair that has not yet accumulated historic information.
摘要翻译： 一些实施方式使用一组广告关键字对的一个或多个历史性能指示符和从该组广告关键字对中提取的一个或多个广告度量来生成映射函数。 可以应用映射函数来映射特定广告关键字对的一个或多个广告度量以确定特定广告关键字对的质量得分。 例如，可以在确定是否选择要显示的广告时使用质量得分，或者可以作为对广告商的反馈来提供质量得分。 另外，在一些实现中，可以应用映射函数来确定尚未累积历史信息的新的广告关键字对的质量得分。
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公开(公告)号：US20080275862A1
公开(公告)日：2008-11-06
申请号：US11743942
申请日：2007-05-03
申请人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
发明人： Tie-Yan Liu , Bin Gao , Wei-Ying Ma
IPC分类号： G06F17/30
CPC分类号： G06K9/6224 , G06F17/3071
摘要： A clustering system generates an original Laplacian matrix representing objects and their relationships. The clustering system initially applies an eigenvalue decomposition solver to the original Laplacian matrix for a number of iterations. The clustering system then identifies the elements of the resultant eigenvector that are stable. The clustering system then aggregates the elements of the original Laplacian matrix corresponding to the identified stable elements and forms a new Laplacian matrix that is a compressed form of the original Laplacian matrix. The clustering system repeats the applying of the eigenvalue decomposition solver and the generating of new compressed Laplacian matrices until the new Laplacian matrix is small enough so that a final solution can be generated in a reasonable amount of time.
摘要翻译： 聚类系统生成表示对象及其关系的原始拉普拉斯矩阵。 聚类系统首先将特征值分解求解器应用于原始拉普拉斯矩阵进行多次迭代。 然后，聚类系统识别所得到的特征向量的元素是稳定的。 然后，聚类系统聚合对应于所识别的稳定元素的原始拉普拉斯矩阵的元素，并形成作为原始拉普拉斯矩阵的压缩形式的新的拉普拉斯矩阵。 聚类系统重复应用特征值分解求解器和生成新的压缩拉普拉斯矩阵，直到新的拉普拉斯矩阵足够小，以便在合理的时间内生成最终解。
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公开(公告)号：US20080147669A1
公开(公告)日：2008-06-19
申请号：US11611113
申请日：2006-12-14
申请人： Tie-Yan Liu , Bin Gao , Guoyang Shen , Wei-Ying Ma , Amit Aggarwal
发明人： Tie-Yan Liu , Bin Gao , Guoyang Shen , Wei-Ying Ma , Amit Aggarwal
IPC分类号： G06F17/30
CPC分类号： G06F16/951
摘要： A method and system for determining whether a web site is a spam web site based on analysis of changes in link information over time is provided. A spam detection system collects link information for a web site at various times. The spam detection system extracts one or more features from the link information that relate to changes in the link information over time. The spam detection system then generates an indication of whether the web site is a spam web site using a classifier that has been trained to detect whether the extracted feature indicates that the web site is likely to be spam.
摘要翻译： 提供一种用于基于对随着时间的链接信息的变化的分析来确定网站是否是垃圾网站的方法和系统。 垃圾邮件检测系统在不同时间收集网站的链接信息。 垃圾邮件检测系统从与链接信息随时间变化相关的链接信息中提取一个或多个特征。 然后，垃圾邮件检测系统使用已经被训练来检测所提取的特征是否指示该网站可能是垃圾邮件的分类器来生成网站是否是垃圾邮件网站的指示。
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10.发明授权公开(公告)号：US07624081B2
公开(公告)日：2009-11-24
申请号：US11392987
申请日：2006-03-28
申请人： Qiankun Zhao , Tie-Yan Liu , Wei-Ying Ma
发明人： Qiankun Zhao , Tie-Yan Liu , Wei-Ying Ma
CPC分类号： G06F17/30882 , G06F17/30864 , G06Q50/01 , Y10S707/99933
摘要： A community mining system analyzes objects of different types and relationships between the objects of different types to identify communities. The relationships between the objects have an associated time. The community mining system extracts various features related to objects of a designated type from the relationships between objects of different types that represent the evolution of the features over time. The community mining system collects training data that indicates extracted features associated with members of the communities. The community mining system then classifies an object of the designated type as being within the community based on closeness of the features of the object to the features of the training data.
摘要翻译： 社区挖掘系统分析不同类型对象的对象，并分析不同类型的对象之间的关系，以识别社区。 对象之间的关系具有相关联的时间。 社区挖掘系统从不同类型的对象之间的关系中提取与指定类型的对象相关的各种功能，这些对象代表随时间推移的特征的演变。 社区采矿系统收集指示与社区成员相关的提取功能的培训数据。 然后，社区挖掘系统根据对象的特征与训练数据的特征的接近性，将指定类型的对象分类为在社区内。
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