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http://dx.doi.org/10.9708/jksci.2011.16.12.033

An efficient approach of avoiding extensions of duplicated graph patterns in cyclic graph mining  

No, Young-Sang (Dept. of Computer Science, Chungbuk National University)
Yun, Un-Il (Dept. of Computer Science, Chungbuk National University)
Pyun, Gwang-Bum (Dept. of Computer Science, Chungbuk National University)
Ryang, Heung-Mo (Dept. of Computer Science, Chungbuk National University)
Lee, Gang-In (Dept. of Computer Science, Chungbuk National University)
Ryu, Keun-Ho (Dept. of Computer Science, Chungbuk National University)
Lee, Kyung-Min (Office of the Texas State Chemist, Texas AgriLife Research, Texas A&M University)
Publication Information
Journal of the Korea Society of Computer and Information / v.16, no.12, 2011 , pp. 33-41 More about this Journal
Abstract
From Complicated graph structures, duplicated operations can be executed and the operations give low efficiency. In this paper, we propose an efficient graph mining algorithm of minimizing the extension of duplicated graph patterns in which the priorities of cyclic edges are considered. In our approach, the cyclic edges with lower priorities are first extended and so duplicated extensions can be reduced. For performance test, we implement our algorithm and compare our algorithm with a state of the art, Gaston algorithm. Finally, We show that ours outperforms Gaston algorithm.
Keywords
Cyclic graph; duplication estimation; Subgraph mining; pattern extension;
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1 J. Han, H. Cheng, D. Xin, and X. Yan, Frequent pattern mining: current status and future directions, Data Mining and Knowledge Discovery, 2007, 15(1), pp. 55-86.   DOI   ScienceOn
2 A. Inokuchi, T. Washio, and H. Motoda, An apri ori-based algorithm for mining frequent substructures from graph data, PAKDD, Sep. 2000, pp. 13-23.
3 U. Kang, D. H. Chau, and C. Faloutsos, Mining large graphs: Algorithms, inference, and discoveries. ICDE, April 2011, pp. 243-254
4 Y. Ke, J. Cheng, J. X. Yu, Top-k Correlative Graph Mining. SDM, May 2009, pp. 1038-1049.
5 M. Kuramochi and G. Karypis, An Efficient Algorithm for Discovering Frequent Subgraphs, TKDE 16(9), pp. 1038-1051, 2004.
6 M. McGlohon, and L. Akoglu, C. Faloutsos. Weighted graphs and disconnected components: Patterns and a generator, ACM SIGKDD, August 2008, pp. 524-532.
7 S. Nijssen, and J. N. Kok, A quickstart in frequent structure mining can make a difference, ACM SIGKDD, August 2004, pp. 647-652.
8 T. Ozaki, and T. Ohkawa, Mining Correlated Subg raphs in Graph Databases. PAKDD, May 2008, pp.272-283.
9 S. Ranu, and A. K. Singh, GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases. ICDE, March 2009, pp.844-855.
10 Y. Xie, and P. S. Yu, Max-Clique: A Top-Down Graph-Based Approach to Frequent Pattern Mining. ICDM, Dec. 2010, pp.1139-1144.
11 X. Yan, and J. Han, gSpan: Graph-based substr ucture pattern mining, ICDM, Dec. 2002, pp. 721-724.
12 X. Yan, and J. Han, CloseGraph: Mining Closed Frequent Graph Patterns, ACM SIGKDD, August 2003, pp. 286-295.
13 F. Zhu, X. Yan, J. Han, and P. S. Yu, gPrune: A Constraint Pushing Framework for Graph Pattern Mining, May PAKDD 2007, pp. 388-400.
14 Z. Zou, J. Li, H. Gao, and S. Zhang, Mining Frequ ent Subgraph Patterns from Uncertain Graph Data. TKDE, 2010, 22(9), pp. 1203-1218.
15 R. Agrawal, T. Imilienski, and A. Swami. "Mining association rules between sets of items in large datasets." In Proceedings of SIGMOD, May, 1993.
16 S. Gunnemann and T. Seidl, Subgraph Mining on Directed and Weighted Graphs. PAKDD, June 2010, pp. 133-146.
17 P. Dmitriev, and C. Lagoze. Mining Generalized Graph Patterns Based on User Examples. ICDM, Dec. 2006. pp. 857-862.