Locating Common Styles Based Totally On Quantitative Binary Attributes Using FP-Growth Algorithm
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Abstract
Discovery of frequent patterns from outsized information is taken into account as a crucial facet of data mining. There is always associate degree ever increasing demand to search out the frequent patterns. This paper introduces a technique to handle the categorical attributes associate degree numerical attributes in an economical means. Within the planned methodology, the ordinary database is reborn into quantitative information and thus it's reborn into binary values reckoning on the condition of the coed information. From the binary patterns of all attributes bestowed within the student information, the frequent patterns are known exploitation FP-growth; the conversion reveals all the frequent patterns within the student database.
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References
Bo Wu, Defu Zhang, Qihua Lan, Jiemin Zheng ,An Efficient Frequent Patterns Mining Algorithm based on Apriori Algorithm and the FP-tree Structure Department of Computer Science, Xiamen University, Xiamen
Lei Want, Xing-Juan Fan2, Xing-Long Lot, Huan Zha Mining data association based on a revised FPgrowth Algorithm Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, Xian, 15- 17 July,
R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDBY94, pp. 487-499.
R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc.1993 ACM-SIGMOD Int. Conf. Management of Data, Washington, D.C., May 1993, pp 207–216
A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. Proceedings of the 21st International Conference on Very large Database,1995.
J.S .Park ,M.S.Chen and P.S.Yu.An effective hashbased algorithm for mining association rules. In SIGMOD1995, pp 175-186.
J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation(PDF), (Slides), Proc. 2000 ACM-SIGMOD Int. May 2000.
A.B.M.Rezbaul Islam, Tae-Sun Chung An Improved Frequent Pattern Tree Based Association Rule Mining Techniques Department of Computer Engineering Ajou University Suwon, Republic of Korea
Agarwal R,Aggarwal C,Prasad V V V.A tree projection algorithm for generation of frequent item sets. In Journal of Parallel and Distributed Computing (Special Issue on High-Performance Data Mining),2000
E. Ramaraj and N. Venkatesan, ― Bit Stream Mask Search Algorithm in Frequent Itemset Mining,‖ European Journal of Scientific Research,‖ Vol. 27 No.2 (2009).
Qihua Lan, Defu Zhang, Bo Wu ,A New Algorithm For Frequent Itemsets Mining Based On Apriori And FP-Tree,Department of Computer Science, Xiamen University, Xiamen China 2009 IEEE