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IJSTR >> Volume 2- Issue 12, December 2013 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Fast Algorithms For Mining Association Rules In Datamining

[Full Text]



P. Usharani





Abstract: We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally di erent from the known algo-rithms. Empirical evaluation shows that these algorithms outperform the known algorithms by factors ranging from three for small problems to more than an order of mag-nitude for large problems. We also show how the best features of the two proposed algorithms can be combined into a hybrid algorithm, called AprioriHybrid. Scale-up experiments show that AprioriHybrid scales linearly with the number of transactions. AprioriHybrid also has ex-cellent scale-up properties with respect to the transaction size and the number of items in the database.



[1]. R. Agrawal, C. Faloutsos, and A. Swami. Ef-cient similarity search in sequence databases. In Proc. of the Fourth International Conference on Foundations of Data Organization and Algo-rithms, Chicago, October 1993.

[2]. R. Agrawal, S. Ghosh, T. Imielinski, B. Iyer, and A. Swami. An interval classi er for database mining applications. In Conference, pages 560{573, Vancouver, British Columbia, Canada, 1992.

[3]. R. Agrawal, T. Imielinski, and A. Swami. Database mining: A performance perspective. IEEE Transactions on Knowledge and Data En-gineering, 5(6):914{925, December 1993. Special Issue on Learning and Discovery in Knowledge-Based Databases.

[4]. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Conference on Management of Data, Washington, D.C., May 1993.

[5]. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. Re-search Report RJ 9839, IBM Almaden Research Center, San Jose, California, June 1994.

[6]. D. S. Associates. The new direct marketing. Business One Irwin, Illinois, 1990.

[7]. R. Brachman et al. Integrated support for data archeology. In AAAI-93 Workshop on Knowledge Discovery in Databases, July 1993.

[8]. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classi cation and Regression Trees. Wadsworth, Belmont, 1984.

[9]. P. Cheeseman et al. Autoclass: A bayesian classi cation system. In 5th Int'l Conf. on Machine Learning. Morgan Kaufman, June 1988.

[10]. D. H. Fisher. Knowledge acquisition via incre-mental conceptual clustering. Machine Learning, 2(2), 1987.

[11]. J. Han, Y. Cai, and N. Cercone. Knowledge discovery in databases: An attribute oriented approach. In Proc. of the VLDB Conference, pages 547{559, Vancouver, British Columbia, Canada, 1992.

[12]. M. Holsheimer and A. Siebes. Data mining: The search for knowledge in databases. Technical Report CS-R9406, CWI, Netherlands, 1994.

[13]. M. Houtsma and A. Swami. Set-oriented mining of association rules. Research Report RJ 9567, IBM Almaden Research Center, San Jose, Cali-fornia, October 1993.

[14]. R. Krishnamurthy and T. Imielinski. Practi-tioner problems in need of database research: Re-search directions in knowledge discovery. SIG-MOD RECORD, 20(3):76{78, September 1991.

[15]. [15] P. Langley, H. Simon, G. Bradshaw, and J. Zytkow. Scienti c Discovery: Computational Explorations of the Creative Process. MIT Press,1987.

[16]. H. Mannila and K.-J. Raiha. Dependency inference. In Proc. of the VLDB Conference, pages 155{158, Brighton, England, 1987.

[17]. H. Mannila, H. Toivonen, and A. I. Verkamo. E cient algorithms for discovering association rules. In KDD-94: AAAI Workshop on Knowl-edge Discovery in Databases, July 1994.

[18]. S. Muggleton and C. Feng. E cient induction of logic programs. In S. Muggleton, editor, Inductive Logic Programming. Academic Press, 1992.

[19]. J. Pearl. Probabilistic reasoning in intelligent systems: Networks of plausible inference, 1992.

[20]. G. Piatestsky-Shapiro. Discovery, analysis, and presentation of strong rules. InG. Piatestsky-Shapiro, editor, Knowledge Discovery in Databases. AAAI/MIT Press, 1991.

[21]. G. Piatestsky-Shapiro, editor. Knowledge Dis-covery in Databases. AAAI/MIT Press, 1991.

[22]. J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufman, 1993.