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