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Proposing an Efficient Method for Frequent Pattern Mining
Abstract:Data mining, which is the exploration of knowledge from the large set of data, generated as a result of the various data processing activities. Frequent Pattern Mining is a very important task in data mining. The previous approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objective. This paper shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This paper will also look for hardware approach of cache coherence to improve efficiency of the above process. The process of data mining is helpful in generation of support systems that can help in Management, Bioinformatics, Biotechnology, Medical Science, Statistics, Mathematics, Banking, Networking and other Computer related applications. This paper proposes the use of both upward and downward closure property for the extraction of frequent item sets which reduces the total number of scans required for the generation of Candidate Sets.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079632Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1608
 R. Agrawal, T. Imielinski, and A.N. Swami, "Mining association rules between sets of items in large databases," Proceedings of ACM SIGMOD International Conference on Management of Data, ACM Press, Washington DC, pp.207-216, May 1993.
 Mohammed J. Zaki, "Scalable Algorithms for Association Mining," IEEE Transactions on Knowledge and Data Engineering, vol.12, no. 3, pp. 372-390, May/June 2000.
 J. Han, J. Pei, and Y. Yin,"Mining Frequent Patterns without Candidate Generation," Proceedings of ACM SIGMOD International Conference on Management of Data, ACM Press, Dallas, Texas, pp. 1-12, May 2000.
 J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, "Hmine: Hyper- Structure Mining of Frequent Patterns in Large Databases," Proceedings of IEEE International Conference on Data Mining, pp. 441-448, 2001.
 Pietracaprina, and D. Zandolin, "Mining Frequent Item sets Using Patricia Tries," FIMI -03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Item set Mining Implementations, Melbourne, Florida, Dec. 2003.
 G. Grahne, and J. Zhu, "Efficiently using prefix-trees in mining frequent itemsets," FIMI -03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida,December 2003.
 Doug Burdick, Manuel Calimlim, Jason Flannick, Johannes Gehrke, "MAFIA: A Maximal Frequent Itemset Algorithm," IEEE Transactions on Knowledge and Data Engineering, vol.17, no. 11, pp. 1490-1505, Nov. 2005.
 M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, "New algorithms for fast discovery of association rules," Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, AAAI Press, pp. 283-286, 1997.
 P. Shenoy, J. R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D. Shah, "Turbo-charging vertical mining of large databases," Proceedings of ACM SIGMOD Intnational Conference on Management of Data, ACM Press, Dallas, Texas, pp. 22-23, May 2000.
 Burdick, M. Calimlim, and J. Gehrke, "MAFIA: a maximal frequent item set algorithm for transactional databases," Proceedings of International Conference on Data Engineering, Heidelberg, Germany, pp. 443-452, April 2001.
 M.J. Zaki, and K. Gouda, "Fast vertical mining using diffsets," Proceedings of the Nineth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington,D.C., ACM Press, New York, pp. 326-335, 2003.
 R. Agrawal, C. Aggarwal, and V. Prasad, "A Tree Projection Algorithm for Generation of Frequent Item Sets," Parallel and Distributed Computing, pp. 350-371, 2000.
 Amol Ghoting,Gregory Buehrer,Srinivasan Parthasarthy,Daehyun Kim,Anthony Nguyen,Yen-Kuang Chen and Pradeep Dubey "Cacheconscious Frequent Pattern Mining on a Modern Processor" Proceedings of the 31st VLDB Conference,Trondheim,Norway,2005.
 Vaibhav Kant Singh and Vijay Shah "Minimizing Space Time Complexity in Frequent Pattern Mining by Reducing Database Scanning and Using Pattern Growth Method" To be appeared in Chhattisgarh Journal of Science & Technology, Coming Volume ISSN 0973-7219.
 Vaibhav Kant Singh and Vinay Kumar Singh "Minimizing Space Time Complexity by RSTDB a new method for Frequent Pattern Mining" To be appeared in Proceeding of the First International Conference on Intelligent Human Computer Interaction ,Allahabad,2009.