Graph-based data clustering via multiscale community detection

Author:

Liu Zijing,Barahona MauricioORCID

Abstract

AbstractWe present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computer Networks and Communications,Multidisciplinary

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