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

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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.