Renyi entropy driven hierarchical graph clustering.

ABSTRACT: This article explores a graph clustering method that is derived from an information theoretic method that clusters points in R n 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 R d 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 exa

SUBMITTER: Oggier F 

PROVIDER: S-EPMC7959621 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature