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High Performance Bit Search Mining Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 14 - Number 2 |
| Year of Publication: 2011 |
| Authors: N. Venkatesan, E. Ramaraj |
10.5120/1817-2371
|
N. Venkatesan, E. Ramaraj . High Performance Bit Search Mining Technique. International Journal of Computer Applications. 14, 2 ( January 2011), 15-21. DOI=10.5120/1817-2371
Abstract
Searching algorithms are closely related to the concept of dictionaries. String searching algorithms are too complex in all sorts of applications. To analyze an algorithm is to determine the amount of resources (such as time and storage) necessary to execute it. Most algorithms are designed to work with inputs of arbitrary length. Usually the efficiency or running time of an algorithm is stated as a function relating the input length to the number of steps (time complexity) or storage locations (space complexity). Time efficiency estimates depend on what defined to be all step. For the analysis to correspond usefully to the actual execution time, the time required to perform a step must be guaranteed to be bounded above by a constant. The main objective of this paper is to reduce the scanning the dataset by introducing new searching technique. So far, arrays, trees, hashing, depth first, breadth first, prefix tree based searching are used in association rule mining algorithms. If the size of the input is large, run time analysis of the algorithm is also increased. In this paper, a novel data structure is introduced so that it reduced dataset scan to one search. This new search technique is bit search. This bit search technique is to find the kth itemsets (where k =1,2,3,……n) in one search scan.
References
- Zhi-Choa Li, Pi-Lian He, Ming Lei, “A High Efficient Aprio,iTID Algorithm for mining Association rule”, Proceedings of 4th International Conference on machine learning and cybernetics, pp 18-21 AUG 2005.
- He Li-jian, Chen Li-chao, Liu shuang-ying, “Improvement of AprioriTid Algorithm for Mining Association Rules”, Journal of Yantai University, Vol.16, No.4, 2003.
- R. Agrawal, J.Shafer, “Parallel mining of association rules”, IEEE Transactions on knowledge and Data Engineering, 8(6), December 1996.
- R. Agrawal, T. Imielinski, and A.N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, volume 22(2) of SIGMOD Record, pages 207–216. ACM Press, 1993.
- Ke Su, Fengsdhan Bai Mining weighted Association Rules IEEE transactions on KDE 489-495, April 2008
- J. Han, J. Pei, and Y. Yin, ”Mining frequent patterns without candidate generation,” Procedings of ACM SIGMOD Intnational 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,” Proc. of IEEE Intl. Conference on Data Mining, pp. 441-448, 2001.
- A. Pietracaprina, and D. Zandolin, ”Mining frequent itemsets using Patricia Tries,” FIMI ’03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, December 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.
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Proceedings 20th International Conference on Very Large Data Bases, pages 487–499. Morgan Kaufmann, 1994.
- C. Borgelt and R. Kruse. Induction of association rules: Apriori implementation. In W. H¨ardle and B. R¨onz, editors, Proceedings of the 15th Conference on Computational Statistics, pages 395–400, http://fuzzy.cs.unimagdeburg.de/~borgelt/software.html2002. Physica-Verlag.
- D. Burdick, M. Calimlim, and J. Gehrke, ”MAFIA: a maximal frequent itemset algorithm for transactional databases,” Proceedings of International Conference on Data Engineering, Heidelberg, Germany, pp. 443-452, April 2001,
- Christian Borgelt Efficient implementation of Apriori and Eclat FIMII ‘03
- Bart Goethals Survey on Frequent Pattern Mining, , HIIT Basic Research Unit, University of Helsinki, Finland.
- Ja-Hwung Su, Wen-Yang Lin: CBW: An efficient algorithm for Frequent Itemset Mining, Proceedings of 37th Hawaii International Conference on System Science 2004.
- Data Mining – Concepts and Techniques, Jiawei Han, Micheline Kamber – 2004 Edn
- Mingju Song and Sanguthevar Rajasekaran A transaction mapping for frequent itemsets mining IEEE transactions on Knowledge and Data Engineering 18(4):472-480, April 2006.
- Balaji Padmanabhan, Alexandar Tuzhilin On Characterization and Discovery of Minimal unexpected pattern in Rule Discovery IEEE trans on KDE vol 18 no. 2 Feb ‘06
- 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.
- 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.
- P. Shenoy, J. R. Haritsa, S. Sudarshan, G. Bhalotia, M. Bawa, and D. Shah, ”Turbo-charging vertical mining of large databases,” Procedings of ACM SIGMOD Intnational Conference on Management of Data, ACM Press, Dallas, Texas, pp. 22-23, May 2000,
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