Enhanced MCL Clustering

  • Mouiad Abid Hani The university of Th-Qar
Keywords: Markov clustering, arkov Chain Model, Repeated Random Walks, Graph Clustering



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.

How to Cite
Abid Hani, M. (2019). Enhanced MCL Clustering. University of Thi-Qar Journal of Science, 3(1), 107-115. Retrieved from https://jsci.utq.edu.iq/index.php/main/article/view/215