DocumentCode :
3022525
Title :
A Novel Clustering Algorithm for Graphs
Author :
Chen, Dongming
Author_Institution :
Software Coll., Northeastern Univ., Shenyang, China
Volume :
4
fYear :
2009
fDate :
7-8 Nov. 2009
Firstpage :
279
Lastpage :
283
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;
fLanguage :
English
Publisher :
ieee
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
Type :
conf
DOI :
10.1109/AICI.2009.31
Filename :
5376354
Link To Document :
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