Enhanced MCL Clustering

Abstract

The goal of graph clustering is to partition vertices in a large graph into different clusters
based on various criteria such as vertex connectivity or neighborhood similarity. Graph
lustering techniques are very useful for detecting densely connected groups in a large graph. In
this research, we introduce a clustering algorithm for graphs; this algorithm is based on Markov
lustering (MCL), which is a clustering method that uses a simulation of stochastic flow. We
have tuned to set the proper factors of inflation, matrix and threshold. Theoretical analysis is
provided to show that the enhanced EMCL-Cluster is converging. Then the proposed method is
ompared with other clustering methods.