An Iterative Indexing PLANO Frame Work for Graph Feature Mining

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Abstract:

Graph indexing is one of the budding dominant current research domains. Varieties of data from different domains pop into the daily updating world in huge volume. There are plenty ways to analyze and to arrange them into an easily accessible index. Here in this paper, we concentrate on the graphical approach. The complete classical algorithms of graph indexing are discussed initially followed by the latest proposed solving methods. Alas, at the end of the paper a complete PLANO frame work is proposed such that the given data can be easily indexed and the updated features can be propelled onto the index.

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1156-1159

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April 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Dayu Yuan, Prasenjit Mitra , Huiwen Yu, C. Lee Giles Iterative Graph Feature Mining for Graph Indexing, IEEE 28th International Conference on Data Engineering, (2012).

DOI: 10.1109/icde.2012.11

Google Scholar

[2] A. Pankaj Moses Monickaraj, K. Vivekanadan, D. Ramyachitra, VFG – INDEX: A Novel Graph Indexing Method, International Journal Of Computer Science And Informatics, ISSN (PRINT): 2231 –5292, Volume‐3, Issue‐2, (2013).

DOI: 10.47893/ijcsi.2014.1185

Google Scholar

[3] A. Pankaj Moses Monickaraj, K. Vivekanandan, D. Ramya Chitra, Bon Iterative Graph Feature Mining for Graph Indexing", Journal of Research in Computing Science, ISSN: 1870-4069, volume 66, (2013).

DOI: 10.4028/www.scientific.net/amm.548-549.1156

Google Scholar

[4] T. Washio and H. Motoda. State of the art of graph-based data mining. SIGKDD Explorations, 5: 59{68, (2003).

DOI: 10.1145/959242.959249

Google Scholar

[5] A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Proc. of 2000 European Symp. Principle of Data Mining and Knowledge Discovery, pages 13{23, 2000).

DOI: 10.1007/3-540-45372-5_2

Google Scholar

[6] M. Kuramochi and G. Karypis. Frequent subgraph discovery. In Proc. of ICDM, pages 313{320, (2001).

Google Scholar