Abstract
In the business analytics association rules plays a vital role. All the business people are concentrating to find the correlations and the associations between the variables in the large databases. Association rule mining is a well researched technique in the area of Data Mining. The association rules are a part of intelligent systems. Association rules provide knowledge to the knowledge workers. Association rules need to satisfy minimum support and minimum confidence at the same time. Minimum support and minimum confidence are the user-specified values. Traditionally the Apriory and the FP-growth algorithms are used to extract association rules. The FP-Growth algorithm is completely depends on frequent pattern tree. In traditional frequent pattern tree, node is labeled only with the current node support count, which consumes more time during traversing, to extract the frequent patterns associated with that particular item. In this research, we are more concentrated on design and implementation of novel algorithms for frequent pattern trees. By using the proposed algorithms the traversal time is reduced. In this paper we present six efficient algorithms.
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References
Agarwal, R., and Srikanth,R., “Fast Algorithms for mining association rules,” In Proc. Of the International Conference on VLDB-94,Sept.1994,pp.487-499.
T.Mitchell.” Machine learning,” Mc Graw Hill, Boston,M.A,1997.
J.Han and m.Kamber. “Data Mining: Concepts and Techniques,” Morgan Kaufmann Publishers, San Francisco, 2001.
Pang-ning-Tan,Vipin Kumar,Michael Steinbach.” Introduction to Data Mining” Pearson 2007. ISBN 978-81-317-1472-0.
Bodon. F, “A Survey on Frequent Itemset Mining”, Technical report, Budapest Univ. Of Technology and Economics, 2006.
Cheung D, V.T Ng, A. Fu, and Y.Fu. “Efficient mining of association rules in distributed databases”. IEEE Trans. Knowledge and Data Engineering, pp 1-23, 1996
Rupali Haldulakar and Prof. Jitendra Agrawal, “Optimization of Association Rule Mining through Genetic Algorithm”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 3 Mar 2011, pp. 1252-1259.
Manish Saggar, Ashish Kumar Agarwal and Abhimunya Lad, “Optimization of Association Rule Mining using Improved Genetic Algorithms”IEEE 2004.
Anandhavalli M, Suraj Kumar Sudhanshu, Ayush Kumar and Ghose M.K., “Optimized association rule mining using genetic algorithm”, Advances in Information Mining, ISSN: 0975–3265, Volume 1, Issue 2, 2009, pp-01-04.
J. J. Sylvester (1878) "On an application of the new atomic theory to the graphical representation of the invariants and covariants of binary quantics, — with three appendices," American Journal of Mathematics, Pure and Applied, (1) : 64-90. The term "graph" first appears in this paper on page 65.
Adelson-Velskii and E. M. Landis, 1962. "An algorithm for the organization of information". Proceedings of the USSR Academy of Sciences 146: 263–266. (Russian) English translation by Myron J. Ricci in Soviet Math. Doklady, 3:1259–1263, 1962.
D.Bujji Babu,Dr.R.Sivarama Prasad and Y.Umamaheswararao “Efficient Frequent Pattern Tree Construction”,International Journal of Advanced Computer Research, Volume -4 Number-1issue-14 March-2014.
Massachusetts Institute of Technology (MIT), "Master Theorem: Practice Problems and Solutions", http://www.csail.mit.edu/~thies/6.046-web/master.pdf.
M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li.New Algorithms for Fast Discovery of Association Rules. Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD’97), 283–296. AAAI Press, Menlo Park, CA, USA 1997.
G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In FIMI’03, Workshop onFrequent Itemset Mining Implementations, November 2003.
F. Masseglia, F. Cathala, and P. Poncelet. Psp : Prefix tree for sequential patterns. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD’98) Nantes France LNAI, pages 176–184, 1998.
Adelson-Velskii and E. M. Landis, 1962. "An algorithm for the organization of information". Proceedings of the USSR Academy of Sciences 146: 263–266. (Russian) English translation by Myron J. Ricci in Soviet Math. Doklady, 3:1259–1263, 1962.
F. Masseglia, F. Cathala, and P. Poncelet. Psp : Prefix tree for sequential patterns. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD’98) Nantes France LNAI, pages 176–184, 1998.
G. Grahne and J. Zhu. Efficiently using prefix-trees in mining frequent itemsets. In FIMI’03, Workshop onFrequent Itemset Mining Implementations, November 2003.
C. Borgelt. Recursion Pruning for the Apriori Algorithm. Proc. 2nd IEEE ICDM Workshop onFrequent Item Set Mining Implementations (FIMI 2003, Brighton, United Kingdom). CEUR Workshop Proceedings 126, Aachen, Germany 2004.http://www.ceur-ws.org/Vol-126/
Leung, C. K. S., Mateo, M. A. F., & Brajczuk, D. A. (2008). A tree-based approach for frequent pattern mining from uncertain data.Lecture Notes in Computer Science, 5012, 653–661.
Acknowledgements
We are so grateful to Sri. Dr.Kancharla Ramaiah garu,Secretary and correspondent of Prakasam Engineering College, kandukur, for extending his marvelous encouragement and support to do the research with providing the research environment. Last but not least, we are very much thankful to all the authors and co-authors of the reference papers for providing us knowledge about clouds, cloud environment and the data mining techniques particularly about association rule learning process and algorithms.
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Prasad, R.S.R., Chakravarthy, N.S.K., Babu, D.B. (2015). Design and Implementation of Novel Algorithms for Frequent Pattern Trees. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_115
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DOI: https://doi.org/10.1007/978-3-319-08422-0_115
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