Volume 8 Number 4 (Apr. 2013)
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JSW 2013 Vol.8(4): 1035-1041 ISSN: 1796-217X
doi: 10.4304/jsw.8.4.1035-1041

A Novel Clustering Algorithm Based on Graph Matching

Guoyuan Lin1, 2, Yuyu Bie1, Guohui Wang1, Min Lei3, 4
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
2State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
3Information Security Center, Beijing University of Posts and Telecommunications, Beijing, China

4National Engineering Laboratory for Disaster Backup and Recovery, Beijing University of Posts and Telecommunications, Beijing, China

Abstract—Aiming at improving current clustering algorithms for their failure to effectively represent high-dimensional data, this paper provides a novel clustering algorithm-GMC-based on graph matching with data objects being represented as the attributed relational graph and the graph matching degree being the standard of similarity measurement. In the algorithm, graphs for classification will be matched with character pattern atlas, and classified into the class with the biggest similarity. The accuracy and rationality of this algorithm is always kept with continuous renewal of character pattern atlas. In addition, compared with the classical K-means clustering algorithm and Newman fast algorithm, this algorithm shows its own superiority and feasibility in applications of data mining.

Index Terms—Clustering analysis, association rules, attributed relational graph, similarity matching, character pattern graph.

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Cite: Guoyuan Lin, Yuyu Bie, Guohui Wang, Min Lei, "A Novel Clustering Algorithm Based on Graph Matching," Journal of Software vol. 8, no. 4, pp. 1035-1041, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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