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Association rule ranking function by decreased lift influence  

Park, Hee-Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.3, 2010 , pp. 397-405 More about this Journal
Abstract
Data mining is the method to find useful information for large amounts of data in database, and one of the important goals is to search and decide the association for several variables. The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function by decreased lift influence to generate association rule for items satisfying at least one of three criteria. We compared our function with the functions suggested by Park (2010), and Wu et al. (2004) using some numerical examples. As the result, we knew that our decision function was better than the function of Park's and Wu's functions because our function had a value between -1 and 1regardless of the range for three association thresholds. Our function had the value of 1 if all of three association measures were greater than their thresholds and had the value of -1 if all of three measures were smaller than the thresholds.
Keywords
Association rule ranking function; confidence; data mining; interest; lift; support;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 Wu, X., Zhang, C. and Zhang, S. (2004). Eficient mining of both positive and negative association rules. ACM Transactions on Information Systems, 22, 381-405.   DOI   ScienceOn
2 Park, J. S., Chen, M. S. and Philip, S. Y. (1995). An effective hash-based algorithms for mining association rules. Proceedings of ACM SIGMOD Conference on Management of Data, 175-186.
3 Pasquier, N., Bastide, Y., Taouil, R. and Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. Proceedings of the 7th International Conference on Database Theory, 398-416.
4 Pei, J., Han, J. and Mao, R. (2000). CLOSET: An efficient algorithm for mining frequent closed itemsets. Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 21-30.
5 Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. Proceedings of the 21st VLDB Conference, 407-419.
6 Toivonen, H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.
7 Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11, 68-77.
8 Han, J., Pei, J. and Yin, Y. (2000). Mining frequent patterns without candidate generation. Proceedings of ACM SIGMOD Conference on Management of Data, 1-12.
9 Liu, B., Hsu, W. and Ma, Y. (1999). Mining association rules with multiple minimum supports. Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining, 337-241.
10 Park, H. C. (2008). The proposition of conditionally pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 19, 1141-1151.   과학기술학회마을
11 Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487-499.
12 Park, H. C. (2010). Development of associative rank decision function using basic association rule thresholds. Journal of the Korean Data Analysis Society, 12, unpublished.
13 Cho, K. H. and Park, H. C. (2007). Association rule mining by environmental data fusion. Journal of the Korean Data & Information Science Society, 18, 279-287.   과학기술학회마을
14 Cho, K. H. and Park, H. C. (2008). A study of association rule application using self-organizing map for fused data. Journal of the Korean Data & Information Science Society, 19, 95-104.
15 Choi, J. H. and Park, H. C. (2008). Comparative study of quantitative data binning methods in association rule. Journal of the Korean Data & Information Science Society, 19, 903-910.   과학기술학회마을
16 Agrawal, R., Imielinski, R. and Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of Data, 207-216.
17 Cai, C. H., Fu, A. W. C., Cheng, C. H. and Kwong, W. W. (1998). Mining association rules with weighted items. Proceedings of International Database Engineering and Applications Symposium, 68-77.
18 Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proceedings of ACM SIGMOD Conference on Management of Data, 85-93.