TY - JOUR ID - TI - Enhanced MCL Clustering AU - Mouiad Abid Hani AU - Kadhem Mahdi Hashem PY - 2011 VL - 3 IS - 1 SP - 107 EP - 115 JO - University of Thi-Qar Journal of Science مجلة علوم ذي قار SN - 19918690 27090256 AB -
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 clustering 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 clustering (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 compared with other clustering methods.

Keywords: Markov clustering (MCL), Markov Chain Model (MCM), Repeated Random Walks (RRW), Graph Clustering (GC).

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