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).
ER -