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A Fuzzy Based Model for Mining Conditional Hybrid Dimensional Association Rules

Neelu Khare, Neeru Adlakha, K. R. Pardasani

Abstract


Mining association rules in transactional or relational databases is an important task in data mining. Fuzzy predicates have been incorporated into association rule mining to extend types of data relationships that can be represented, for interpretation of rules in linguistic terms and to avoid fix boundaries in partitioning data attributes. In this paper, the mining of single dimensional association rule and non-repetitive predicate multi-dimensional association rule are combined over the transactions of multidimensional transaction database. The algorithm mines conditional hybrid dimension association rules which satisfy the definite condition on the basis of multi-dimensional transaction database. In this algorithm each predicate should be partitioned at the fuzzy set level, the support count of itemsets is calculated by performing fuzzy AND operation on items that constitute the itemsets. Apriori property is used in algorithm to prune the item sets. The implementation of algorithm is illustrated with the help of a simple example.

Keywords


Fuzzy Set Level, Hybrid Dimensional Association Rule, Multi-Dimensional Transaction Database, Conditional Restrict, Predicate.

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References


Agrawal, R., Imielinski, T., and Swami, A. N. “Mining association rules between sets of items in large databases”, In Proceedings of the ACM SIGMOD International Conference on Management of Data, 1993 pp-207-216.

Agrawal, R. and Srikant,” Fast algorithms for mining association rules”, In Proc. 20th Int. Conf. Very Large Data Bases, 1994, pp-487-499,.

J. Han, J. Pei, Y. Yin, “Mining frequent patterns without candidate generation”, Proc. ACM SIGMOD Int. Conf. Management of Data SIGMOD May 2000, pp- 1–12.

Kuen-Fang Jea, Ming-Yuan Chang, O.V. Mazurin and E.A. Porai-Koshits (eds.),” Discovering frequent itemsets by support approximation and itemset clustering”, Data & Knowledge Engineering, 2008, pp- 90–107.

ZHi-jie LI, Fei-xue HUANG, Dong-qing ZHOU, Peng ZHANG, “Using Data Cube for Mining of Hybrid Dimensional Association Rules”, GCC 2003 LCNS3033 © Springer – Verlag Berlin Heidelberg 2004, pp-899-902.

Yan Xin, Shi-Guang Ju , “Mining Conditional Hybrid-Dimension Association Rules On The Basis Of Multi-Dimensional Transaction Database”, Proceedings of the Second International Conference on Machine Learning and Cybernetics, ,November 2003, pp- 216-221.

Wan-xin xu, ru-jing wang,” A fast algorithm of mining multidimensional association Rules frequently”, , Chinese Academy of Sciences, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, August 2006, pp-13-16.

Jiawei Han, Micheline Kamber, “Data Mining Concepts and Techniques”. Higher Education Press 2001.

Pang Ning Tan, Michael Steinbach, Vipin Kumar “Introduction to Data Mining” Pearson Education 2007.

Klemetinen, L., Mannila, H., Ronkainen, P., “Finding interesting rules from large sets of discovered association rules”. Third International Conference on Information and Knowledge Management Gaithersburg, USA 1994, pp-401-407.

Reda ALHAJJ and Mehmet KAYA, "Integrating Fuzziness into OLAP for Multidimensional Fuzzy Association Rules Mining", IEEE International Conference on Data Mining, Melbourne, FL, USA, November, 2003, pp- 19-22.

Anjna Pandey, K. R. Pardasani, Rough Set Model for Discovering Hybrid Dimensional Association Rules, International Journal of Computer Science and Network Security, Vol 9, No.6, 2009, pp159-164.

G. J. Klir, B. Yuan,” Fuzzy Sets and Fuzzy Logic: Theory and Applications”, New Jersey: Prentice Hall, 1995.

Jurgen M. Jams Fakultat fur Wirtschafts, “An Enhanced Apriori Algorithm for Mining Multidimensional Association Rules”, 25th Int. Conf. Information Technology interfaces ITI 2003, pp-193-198.

Rolly Intan, “A Proposal Of Fuzzy Multidimensional Association Rules”, Jurnal Informatika Vol 7,2006, pp- 85-90.

R. Srikant and R. Agrawal, "Mining Generalized Association Rules", ... Proc. of the 20th Int'l Conference on Very Large Databases, 1995, Vol 37, pp 49-61.

Neelu Khare, Neeru Adlakha and K. R. Pardasani, “An Algorithm for Mining Multidimensional Fuzzy Association Rules”, International Journal of Computer Science and Information Security, Vol 5, No.1, September 2009, pp-72-76.

Neelu Khare, Neeru Adlakha and K. R. Pardasani, “An Algorithm for Mining Conditional Hybrid Dimensional Association Rules using Boolean Matrix”, Conference proceeding ICCAE 2010 Vol 2, IEEE Xplore pp-644-648.

F. Berzal, J.C. Cubero, N. Marin, J.M. Serrano, TBAR: “An efficient method for association rule mining in relational databases”, Elsevier Data & Knowledge, Engineering 37, 2001, pp-47–64.

S. Brin, R. Motwani and C. Silverstein,” Beyond Market Basket: Generalizing Association Rules to Correlations”, Proc. ACM SIGMOD Int. Conf. Management of Data,1997,pp-265-276.

J. S. Park, M. S. Chen and P.S. Yu “An effective hash based algorithm for mining association rules”, SIGMOD 1995, 175-186.

Hanbbing Liu and Baisheng Wang “ An association rule mining algorithm based on a Boolean matrix” Data Science Journal , vol 6, Supplement 9, September 2007,pp-559-564.


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