Abstract
Data mining is the field in which the most of the new researches and discoveries are being made and in it frequent mining of itemsets is the very critical and preliminary task. Apriori is the algorithm which is mostly used for this very purpose. Apriori also suffers from some problems such as finding the support count, which is a very time consuming procedure. To overcome the above stated problem BitApriori algorithm was devised. Though this problem was eradicated, but this algorithm suffers from a memory scarcity problem and to overcome this problem in the paper here a new Enhanced BitApriori algorithm is devised which performs better than its predecessors through the experimental results.
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References
Zheng, J.: An efficient algorithm for frequent itemsets in data mining: Department of Management Sciences. City University of Hong Kong, Hong Kong (2010)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: The International Conference on Very Large Databases, pp. 487–499 (1994)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, pp. 283–296 (1997)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1–12. ACM Press (2000)
Pork, J.S., Chen, M.S., Yu, P.S.: An effective hash based algorithm for mining association rules. ACM SIGMOD, 175–186 (1995)
Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 255–264 (1997)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, Tuscon, Arizona, pp. 265–276 (1997)
Dong, J., Han, M.: BitTableFI an efficient mining frequent itemsets algorithm. Knowledge Based Systems 20(4), 329–335 (2007)
Frequent Itemset Mining Implementations Repository, http://fimi.cs.helsinki.fi
Kantardzic, M.: Data mining Concepts, Models, Methods, and Algorithms. Wiley Inter-science, NJ (2003)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)
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Khan, Z., Haseen, F., Rizvi, S.T.A., ShabbirAlam, M. (2015). Enhanced BitApriori Algorithm: An Intelligent Approach for Mining Frequent Itemset. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_37
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DOI: https://doi.org/10.1007/978-3-319-11933-5_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11932-8
Online ISBN: 978-3-319-11933-5
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