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    • 7. 发明授权
    • Clustering customers
    • 聚集客户
    • US08914372B2
    • 2014-12-16
    • US13432361
    • 2012-03-28
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • G06F17/30G06Q30/02
    • G06Q30/02
    • A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the tune attribute.
    • 用于聚类客户的计算机实现方法包括接收客户记录的源集合,其中每个客户记录表示一个客户,并且每个客户记录包括至少一个数据属性,并且每个数据属性具有属性值; 预处理客户记录的源集合以生成预处理的一组客户记录; 在预处理的客户记录集上执行聚类算法,以将预处理的客户记录集合分组成预定义数量的集群。 预处理包括:确定客户记录源组中客户的类型; 使用类型属性值来指示客户记录中客户的类型; 归一化数据属性值和类型属性值; 分别对数据属性值和类型属性值进行加权,以获得数据属性的加权属性值和调整属性的加权属性值。
    • 10. 发明授权
    • Deriving a nested chain of densest subgraphs from a graph
    • 从图中导出最密集子图的嵌套链
    • US08799192B2
    • 2014-08-05
    • US13406843
    • 2012-02-28
    • Bin ZhangMeichun Hsu
    • Bin ZhangMeichun Hsu
    • G06F15/18
    • G06F17/30958
    • A nested chain of densest subgraphs is derived by a computer from a given graph that has multiple vertices and edges. The two ends of each edge are assigned with respective incident weights, and each vertex is given a vertex weight. A weight balancing process is carried out by the computer to iteratively go through the edges to adjust the incident weights of each edge and the vertex weights of the vertices connected by that edge to reduce a difference between the vertex weights of the two vertices. After the balancing, the vertex weights are put in an ordered sequence according to their values, and a nested chain of densest subgraphs is derived from the ordered sequence.
    • 密集子图的嵌套链由计算机从具有多个顶点和边的给定图导出。 每个边缘的两端被分配有相应的事件权重,并且每个顶点被赋予顶点权重。 计算机进行权重平衡处理,以迭代地遍历边缘,以调整每个边缘的入射权重和由该边连接的顶点的顶点权重,以减少两个顶点的顶点权重之间的差异。 在平衡之后,顶点权重根据它们的值被置于有序序列中,并且从有序序列导出嵌套的密集子图链。