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METHOD FOR DATA CLUSTERING AND CLASSIFICATION BY A GRAPH THEORY MODEL -- NETWORK PARTITION INTO HIGH DENSITY SUBGRAPHS

机译:图形理论模型的数据聚类与分类方法-网络划分成高密度子图

摘要

A computer based method is provided for clustering related data representing objects of interest and information about levels of relatedness between objects. A weighted graph G is established on a computer. The graph has vertices and weighted edges joining pairs of vertices. Using the computer, the method finds all possible subgraphs H of G satisfying the following dynamic "edge-to-vertex" ratio (I): where the minimum is taken over all possible partitions P of the vertex set of H, and E(H/P) is the set of edges crossing between parts of P. The subgraphs H found are identified as a level-k community if they are maximal, which means that there are no larger subgraphs containing it that satisfy the dynamic "edge-to-vertex" ratio for the same k. All level-k communities are output.
机译:提供了一种基于计算机的方法,用于对表示感兴趣对象的相关数据和有关对象之间的相关性级别的信息进行聚类。在计算机上建立加权图G。该图具有顶点和连接成对顶点的加权边。使用计算机,该方法找到满足以下动态“边到顶点”比率(I)的G的所有可能子图H:其中,对H顶点集的所有可能分区P取最小值,以及E(H / P)是P的各个部分之间相交的一组边。如果找到的子图H最大,则将其标识为k级社区,这意味着没有更大的子图包含满足它的动态“边到边”。相同k的“顶点”比率。输出所有k级社区。

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