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Renyi entropy driven hierarchical graph clustering
PeerJ Computer Science ( IF 3.8 ) Pub Date : 2021-02-25 , DOI: 10.7717/peerj-cs.366
Frédérique Oggier 1 , Anwitaman Datta 2
Affiliation  

This article explores a graph clustering method that is derived from an information theoretic method that clusters points in ${{\mathbb{R}}^{n}}$Rn relying on Renyi entropy, which involves computing the usual Euclidean distance between these points. Two view points are adopted: (1) the graph to be clustered is first embedded into ${\mathbb{R}}^{d}$Rd for some dimension d so as to minimize the distortion of the embedding, then the resulting points are clustered, and (2) the graph is clustered directly, using as distance the shortest path distance for undirected graphs, and a variation of the Jaccard distance for directed graphs. In both cases, a hierarchical approach is adopted, where both the initial clustering and the agglomeration steps are computed using Renyi entropy derived evaluation functions. Numerical examples are provided to support the study, showing the consistency of both approaches (evaluated in terms of F-scores).

中文翻译:

Renyi熵驱动的层次图聚类

本文探讨了一种基于信息理论方法的图聚类方法,该方法基于Renyi熵对$ {{\ mathbb {R}} ^ {n}} $ Rn中的点进行聚类,其中涉及计算这些点之间的通常欧几里德距离。采用两个观点:(1)首先将要聚类的图嵌入到某个维度d的$ {\ mathbb {R}} ^ {d} $ Rd中,以最大程度地减少嵌入的失真,然后生成结果点(2)使用无向图的最短路径距离和有向图的Jaccard距离的变化作为距离,直接对图进行聚类。在这两种情况下,均采用分层方法,其中使用Renyi熵导出的评估函数来计算初始聚类和聚集步骤。
更新日期:2021-02-25
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