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Association rule ranking function using conditional probability increment ratio  

Park, Hee-Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.4, 2010 , pp. 709-717 More about this Journal
Abstract
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 using conditional probability increment ratio. We compared our function with several association rule ranking functions by some numerical examples. As the result, we knew that our decision function was better than the existing functions. The reasons were that the proposed function of the reference value is not affected by a particular association threshold, and our function had a value between -1 and 1 regardless of the range for three association thresholds. And we knew that the ranking function using conditional probability increment ratio was very well reflected in the difference between association rule measures and the minimum association rule thresholds, respectively.
Keywords
Association rule ranking function; conditional probability increment ratio; confidence; lift; support;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 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
2 Wu, X., Zhang, C. and Zhang, S. (2004). Efficient mining of both positive and negative association rules. ACM Transactions on Information Systems, 22, 381-405.   DOI   ScienceOn
3 Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proceedings of ACM SIGMOD Conference on Management of Data, 85-93.
4 Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11, 68-77.
5 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.
6 Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487-499.
7 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.
8 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.
9 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
10 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.
11 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.
12 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
13 Park, H. C. (2010a). Development of associative rank decision function using basic association rule thresholds. Journal of the Korean Data Analysis Society, 12, to appear.
14 Park, H. C. (2010b). Association rule ranking function by decreased lift influence. Journal of the Korean Data & Information Science Society, 21, unpublished.   과학기술학회마을
15 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.
16 Zhou, L. and Yau, S. (2007). Efficient association rule mining among both frequent and infrequent items. Computers and Mathematics with Applications, 54, 737-749.   DOI   ScienceOn
17 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.
18 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.
19 Toivonen H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.
20 Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. Proceedings of the 21st VLDB Conference, 407-419.