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    • 41. 发明授权
    • Focused community discovery in network
    • 在网络中聚焦社区发现
    • US07844634B2
    • 2010-11-30
    • US11283383
    • 2005-11-18
    • Kirsten Weale HildrumPhilip Shi-Lung Yu
    • Kirsten Weale HildrumPhilip Shi-Lung Yu
    • G06F7/00G06F17/30
    • G06F17/30864G06Q10/10
    • Techniques for community discovery in a network are disclosed. For example, a technique for discovering a community around a given entity in an interaction graph, wherein nodes in the graph represent entities and edges connecting nodes in the graph represent interactions between connected nodes, comprises the following steps/operations. Nodes in the interaction graph are partitioned into different sets of nodes based on interaction information associated with each node to minimize a number of interaction pairs that need to be considered. An objective function is minimized by moving entities between the different sets such that the community is discovered once a measure associated with the objective function is minimized.
    • 公布了网络中社区发现的技术。 例如,用于在交互图中发现给定实体周围的社区的技术,其中图中的节点表示连接图中的节点的实体和边表示连接的节点之间的交互,包括以下步骤/操作。 基于与每个节点相关联的交互信息将交互图中的节点划分成不同的节点集合,以最小化需要考虑的多个交互对。 通过在不同组之间移动实体来最小化目标函数,使得一旦与目标函数相关联的度量被最小化,则发现社区。
    • 43. 发明授权
    • Methods and apparatus for generating decision trees with discriminants and employing same in data classification
    • 用于生成具有歧视性的决策树并在数据分类中采用相同的方法和装置
    • US07716154B2
    • 2010-05-11
    • US11841221
    • 2007-08-20
    • Charu C. AggarwalPhilip Shi-Lung Yu
    • Charu C. AggarwalPhilip Shi-Lung Yu
    • G06N5/00
    • G06K9/6282G06F17/3061G06F2216/03Y10S707/99936
    • Methods and apparatus are provided for generating a decision trees using linear discriminant analysis and implementing such a decision tree in the classification (also referred to as categorization) of data. The data is preferably in the form of multidimensional objects, e.g., data records including feature variables and class variables in a decision tree generation mode, and data records including only feature variables in a decision tree traversal mode. Such an inventive approach, for example, creates more effective supervised classification systems. In general, the present invention comprises splitting a decision tree, recursively, such that the greatest amount of separation among the class values of the training data is achieved. This is accomplished by finding effective combinations of variables in order to recursively split the training data and create the decision tree. The decision tree is then used to classify input testing data.
    • 提供了用于使用线性判别分析生成决策树并且在分类(也称为分类))中实现这样的决策树的方法和装置。 数据优选地以多维对象的形式,例如包括决策树生成模式中的特征变量和类变量的数据记录,以及仅包括决策树遍历模式中的特征变量的数据记录。 例如,这种创造性的方法创建更有效的监督分类系统。 通常,本发明包括分解决策树,递归地分割,使得实现训练数据的类值之间的最大分离量。 这是通过找到变量的有效组合来实现的,以便递归地分割训练数据并创建决策树。 然后使用决策树对输入测试数据进行分类。
    • 44. 发明申请
    • SYSTEMS AND METHODS FOR COMPUTATION OF OPTIMAL DISTANCE BOUNDS ON COMPRESSED TIME-SERIES DATA
    • 用于计算压缩时间序列数据的最佳距离边界的系统和方法
    • US20090204574A1
    • 2009-08-13
    • US12027294
    • 2008-02-07
    • Michail VlachosPhilip Shi-Lung Yu
    • Michail VlachosPhilip Shi-Lung Yu
    • G06F17/30
    • G06F17/30548G06F2216/03
    • There are provided a method and a system for computation of optimal distance bounds on compressed time-series data. In a method for similarity search, the method includes the step of transforming sequence data into a compressed sequence represented by top-k coefficients of the sequence data and a sum of the energy of omitted coefficients of the sequence data. The method further includes the step of computing at least one of a lower bound and an upper bound on a distance range between a query sequence and the compressed sequence, given a first and a second constraint. The first constraint is that a sum of squares of the omitted coefficients is less than a sum of the energy of the omitted coefficients. The second constraint is that the energy of the omitted coefficients is less than the energy of a lowest energy one of the top-k coefficients.
    • 提供了一种用于在压缩时间序列数据上计算最佳距离界限的方法和系统。 在相似搜索的方法中,该方法包括将序列数据变换为由序列数据的顶部k个系数表示的压缩序列和序列数据的省略系数的能量之和的步骤。 该方法还包括在给定第一和第二约束的情况下,计算查询序列和压缩序列之间的距离范围上的下限和上限中的至少一个的步骤。 第一个约束是省略的系数的平方和小于所省略的系数的能量之和。 第二个约束是省略的系数的能量小于顶部k系数中最低能量的能量。