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A Framework for Mining Weighted Association Rule Using Hits Progress: Fuzzy Approach

R. Lokesh Kumar, Dr. P. Sengottuvelan

Abstract


Data mining is to extract useful information from a vast amount of data, typically a large database. Association rule mining is a key issue in data mining, which follows link analysis technique. The goal of this technique is to detect relationships or associations between specific values of categorical variables in large data sets. This is a common task in many data mining projects, however the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. It takes the quality of transactions into consideration using link-based models. W-support can be worked out without much overhead, and interesting patterns may be discovered through this new measurement. Next WARM is discussed then the evaluation of transactions with HITS, followed by the definition of w-support and the Apriori mining algorithm. In this paper, a new measure w-support, which does not require preassigned weights, can be used to work on databases with only binary attributes.

Keywords


Association Rule Mining, Fuzzy Mining, HITS, WARM.

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References


Ke Sun and Fengshan Bai, “Mining Weighted Association Rules without Preassigned Weights”, IEEE Transactions on Knowledge and Data Engineering”, VOL. 20, NO. 4, APRIL 2008.

R. Agrawal, T. Imielinski and A. Swami, “Mining Association Rules between Sets of Items in Large Datasets”, Proc. ACM SIGMOD’ 93, pp.207-216, 1993.

J.M. Kleinberg, “Authoritative Sources in a Hyperlinked Environment,” J. ACM, vol.46, no.5, pp. 604-632, 1999.

R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proc. 20th Int’l Conf. Very Large Data Bases (VLDB ’94), pp. 487-499, 1994.

C.H. Cai, A.W.C. Fu, C.H. Cheng, and W.W. Kwong, “Mining Association Rules with Weighted Items”, Proc. IEEE Int’l Database Eng. and Applications Symp. (IDEAS ’98), pp. 68-77, 1998.

G.D. Ramkumar, S. Ranka, and S. Tsur, “Weighted Association Rules: Model and Algorithm”, Proc. ACM SIGKDD, 1998.

B. Liu, W. Hsu, and Y. Ma, “Integrating Classification and Association Rule Mining,” Proc. ACM SIGKDD ’98, pp. 80-86, 1998.

F. Bodon, “A Survey on Frequent Itemset Mining,” technical report, Budapest Univ. of Technology and Economics, 2006.

K. Wang and M.-Y. Su, “Item Selection by “Hub-Authority” Profit Ranking,” Proc. ACM SIGKDD, 2002.

W. Wang, J. Yang, and P.S. Yu, “Efficient Mining of Weighted Association Rules (WAR),” Proc. ACM SIGKDD ’00, pp. 270-274, 2000.

J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proc. ACM SIGMOD, 2000.

J. Li, B. Tang, and N. Cercone, “Applying Association Rules for Interesting Recommendations Using Rule Templates,” Proc. Eighth Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD ’04), pp. 166-170, 2004.

R. Agarwal, C. Aggarwal, and V. V. V. Prasad “A Tree Projection Algorithm for Generation of Frequent Itemsets” Journal of Parallel and Distributed Computing , 2000.

F. Tao, F. Murtagh, and M. Farid, “Weighted Association Rule Mining Using Weighted Support and Significance Framework,” Proc. ACM SIGKDD ’03, pp. 661-666, 2003.

Wengdong Wang , Susan M. Bridges “Genetic Algorithm Optimization of Membership Functions for Mining Fuzzy Association Rules “ Proc Fuzzy Theory and Technology Conference, Atlantic City, N.J. March 2, 2007.

Yi-Chung Hu , Ruey shun chen “ A Fuzzy Data Mining Algorithm for Finding Sequential patterns” ,Proc Internation Journal on Fuzzy and Knowledge based Systems Vol.11,No.2 March 2006.

O. Kurland and L. Lee, “Respect My Authority! HITS without Hyperlinks, Utilizing Cluster-Based Language Models,” Proc. ACM SIGIR, 2006.

H. Cherfi, A. Napoli, and Y. Toussaint, “Towards a Text Mining methodology Using Frequent Itemsets and Association Rule Extraction,”Proc. Fourth Int’l Conf. Journe´es de l’Informatique Messine (JIM ’03) on Knowledge Discovery and Discrete Math.,pp. 285-294, 2003.


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