Title :
A Novel Clustering Algorithm for Graphs
Author_Institution :
Software Coll., Northeastern Univ., Shenyang, China
Abstract :
Graph or network clustering is one of the fundamental multimodal combinatorial problems that have many applications in computer science. Many algorithms have been devised to obtain a reasonable approximate solution for the problem. Current approaches, however, suffer from the local optimum drawback and then have difficulty splitting two clusters with very confused structures. In this paper we propose a novel genetic-based algorithm incorporating with modularity(QN) for the quality of partitioning of graphs. The theoretical analysis and experimental results on synthetic and real networks demonstrate superior performance over Newman´s fast agglomerative algorithms in accuracy.
Keywords :
approximation theory; genetic algorithms; graph theory; network theory (graphs); pattern clustering; approximate solution; computer science; genetic-based algorithm; graph clustering; graph partitioning; local optimum drawback; multimodal combinatorial problems; network clustering; Algorithm design and analysis; Application software; Artificial intelligence; Clustering algorithms; Computational intelligence; Educational institutions; Electronic mail; Genetics; Partitioning algorithms; Software algorithms;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.31