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Enhanced MCL Clustering | ||
| JOURNAL OF THI-QAR SCIENCE | ||
| Article 1, Volume 3, Issue 1, 2013, Pages 107-115 | ||
| Authors | ||
| Mouiad Abid Hani; Kadhem Mahdi Hashem | ||
| Abstract | ||
| AbstractThe 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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