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A Comparative Study of Discovering Frequent Subgraphs – Approaches and Techniques

B.Senthilkumaran, Dr.K.Thangadurai, , ,
Affiliations
P.G. and Research,Department of Computer Science, Government Arts College (Autonomous), Karur-05.
:-NA-


Abstract
Graph mining is an important research vertical and recently the usage of graphs has become increasingly imperative in modeling problematic complex structures such as electrical circuits, chemical compounds, protein structures, bioinformatics, social networks, workflow diagrams, and XML documents. Plethora of graph mining algorithms has been developed and the primary objective of this paper is to present a detailed survey regarding the approaches and techniques employed to find the issues and complexities involved.


Citation
B.Senthilkumaran, Dr.K.Thangadurai, “A Comparative Study of Discovering Frequent Subgraphs – Approaches and Techniques”, International Journal Of Computer Engineering In Research Trends, 4(1):41-45, January-2017. [InnoSpace-2017:Special Edition]


Keywords : Graph, Mining, complex structure, techniques, modelling

References
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[3] Nijssen, S. and Kok, J., Faster association rules for multiple relations. In IJCAI’01:  Seventeenth International Joint Conference on Artificial Intelligence, 2001, vol. 2, pp. 891–896.
[4] Chuntao Jiang, Frans Coenen and Michele Zito, A Survey of Frequent Sub-graph Mining Algorithms:The Knowledge Engineering Review, Vol. 00:0, 1–31.c 2004.
[5] A. Inokuchi, T.Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In PKDD’00.
[6] J. Huan, W.Wang, and J. Prins. Efficient mining of frequent subgraph in the presence of isomorphism. UNC computer science technique report TR03-021, 2003.  
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[8] M. Kuramochi and G. Karypis. Grew-a scalable frequent subgraph discovery algorithm. In ICDM, pages 439–442,2004.
[9] ZhaonianZou, Jianzhong Li, Hong Gao, and Shuo Zhang : Frequent Subgraph Patterns from Uncertain Graph Data. IEEE Transactions On Knowledge And Data Engineering, Vol. 22, No. 9, September 2010.
[10] L. T. Thomas, S. R. Valluri, and K. Karlapalem. Margin:Maximal frequent subgraph mining. Proc. 6th IEEE Int’l Conf. Data mining (ICDM ’06), pp. 1097-1101, 2006.
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[12] Huan, J., Wang, W. and Prins, J. 2003. Efficient Mining of Frequent Subgraph in the Presence of Isomorphism, In Proceedings of the 2003 International Conference on Data Mining, 549-552.



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