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    • 3. 发明申请
    • TECHNOLOGIES FOR NODE-DEGREE BASED CLUSTERING OF DATA SETS
    • 基于节点度的数据集聚技术
    • WO2018057161A2
    • 2018-03-29
    • PCT/US2017/047326
    • 2017-08-17
    • INTEL CORPORATION
    • SITIK, Ahmet C.MORE, Ankit
    • G06F17/30
    • Technologies for node-degree based clustering include a computing device to construct a graph that includes multiple vertices corresponding to the data points of a data set. The computing device inserts an edge between each pair of vertices that has a corresponding similarity metric that meets a predetermined threshold similarity metric. The computing device determines a node degree for each vertex in the graph and initializes a cutoff node degree as the lowest node degree of the vertices. The computing device selects a test subset of the graph that includes vertices having a node degree less than or equal to the cutoff node degree. The computing device determines whether the test subset covers the graph and if not increases the cutoff node degree. If the test subset covers the graph, the data points corresponding to the vertices of the test subset are the representative cluster. Other embodiments are described and claimed.
    • 用于基于节点度的聚类的技术包括计算设备来构建包括与数据集的数据点对应的多个顶点的图。 计算设备在具有符合预定阈值相似性度量的对应相似性度量的每对顶点之间插入边缘。 计算设备确定图中每个顶点的节点度并将截断节点度初始化为顶点的最低节点度。 计算设备选择包括具有小于或等于截止节点度的节点度的顶点的图的测试子集。 计算设备确定测试子集是否覆盖图,并且如果不是,则增加截断节点度。 如果测试子集覆盖该图,则与测试子集的顶点对应的数据点是代表性聚类。 描述并要求保护其他实施例。