Computer implemented scalable, incremental and parallel clustering based on weighted divide and conquer
    1.
    发明授权
    Computer implemented scalable, incremental and parallel clustering based on weighted divide and conquer 有权
    基于加权分割和征服的计算机实现可扩展,增量和并行聚类

    公开(公告)号:US06684177B2

    公开(公告)日:2004-01-27

    申请号:US09854212

    申请日:2001-05-10

    CPC classification number: G06K9/6218 Y10S707/99936 Y10S707/99937

    Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , SP; 2) for each piece Si, determining a set Di of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set Di by the number of points in the corresponding piece Si assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

    Abstract translation: 一种使用加权分割和征服方法来聚集n个数据点的集合S以找到k个最终中心的技术。 该技术包括:1)将集合S划分成P个不相交的部分S1。 。 。 ,SP; 2)对于每个块Si,确定k个中心的集合Di; 3)将每个片段Si中的每个数据点分配给k个中间的最近的一个; 4)通过分配给该中心的相应片段Si中的点的数量对每个集合Di中的每个k个中间中心进行加权; 和5)将加权中间体聚类在一起以找到所述k个最终中心,使用特定的误差度量和聚类方法A进行聚类。

    Computer implemented scalable, incremental and parallel clustering based on weighted divide and conquer
    2.
    发明授权
    Computer implemented scalable, incremental and parallel clustering based on weighted divide and conquer 有权
    基于加权分割和征服的计算机实现可扩展,增量和并行聚类

    公开(公告)号:US06907380B2

    公开(公告)日:2005-06-14

    申请号:US10726254

    申请日:2003-12-01

    CPC classification number: G06K9/6218 Y10S707/99936 Y10S707/99937

    Abstract: A technique that uses a weighted divide and conquer approach for clustering a set S of n data points to find k final centers. The technique comprises 1) partitioning the set S into P disjoint pieces S1, . . . , Sp; 2) for each piece Si, determining a set Di of k intermediate centers; 3) assigning each data point in each piece Si to the nearest one of the k intermediate centers; 4) weighting each of the k intermediate centers in each set Di by the number of points in the corresponding piece Si assigned to that center; and 5) clustering the weighted intermediate centers together to find said k final centers, the clustering performed using a specific error metric and a clustering method A.

    Abstract translation: 一种使用加权分割和征服方法来聚集n个数据点的集合S以找到k个最终中心的技术。 该技术包括:1)将集合S划分成P个不相交的部分S 1。 。 。 ,S 2)对于每个块S i确定k个中间中心的集合D i i i i, 3)将每个片段S i中的每个数据点分配给k个中间中心中最接近的一个; 4)通过分配给该中心的相应片段S i i中的点的数量对每个集合D i i i中的每个k个中间中心进行加权; 和5)将加权中间体聚类在一起以找到所述k个最终中心,使用特定的误差度量和聚类方法A进行聚类。

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