Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.4.835

Exploration of relationship between confirmation measures and association thresholds  

Park, Hee Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.4, 2013 , pp. 835-845 More about this Journal
Abstract
Association rule of data mining techniques is the method to quantify the relevance between a set of items in a big database, andhas been applied in various fields like manufacturing industry, shopping mall, healthcare, insurance, and education. Philosophers of science have proposed interestingness measures for various kinds of patterns, analyzed their theoretical properties, evaluated them empirically, and suggested strategies to select appropriate measures for particular domains and requirements. Such interestingness measures are divided into objective, subjective, and semantic measures. Objective measures are based on data used in the discovery process and are typically motivated by statistical considerations. Subjective measures take into account not only the data but also the knowledge and interests of users who examine the pattern, while semantic measures additionally take into account utility and actionability. In a very different context, researchers have devoted a lot of attention to measures of confirmation or evidential support. The focus in this paper was on asymmetric confirmation measures, and we compared confirmation measures with basic association thresholds using some simulation data. As the result, we could distinguish the direction of association rule by confirmation measures, and interpret degree of association operationally by them. Futhermore, the result showed that the measure by Rips and that by Kemeny and Oppenheim were better than other confirmation measures.
Keywords
Association rule; confidence; confirmation measure; interestingness measure; lift; support;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 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.
2 Cho, K. H. and Park, H. C. (2011). Discovery of insignificant association rules using external variable. Journal of the Korean Data Analysis Society, 13, 1343-1352.
3 Crupi, V., Tentori, K. and Gonzalez, M. (2007). On Bayesian measures of evidential support: Theoretical and empirical issues. Philosophy of Science, 74, 229-252.   DOI   ScienceOn
4 Freitas, A. (1999). On rule interestingness measures. Knowledge-based System, 12, 309-315.   DOI   ScienceOn
5 Geng, L. and Hamilton, H. J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38, 1-32.   DOI   ScienceOn
6 Glass, D. H. (2013). Confirmation measures of association rule interestingness. Knowledge-Based Systems, 44, 65-77.   DOI   ScienceOn
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 Hilderman, R. J. and Hamilton, H. J. (2000). Applying objective interestingness measures in data mining systems. Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, 432-439.
10 Kemeny, J. G. and Oppenheim, P. (1952). Degree of factual support. Philosophy of Science, 19, 307-324.   DOI   ScienceOn
11 Lim, J., Lee, K. and Cho, Y. (2010). A study of association rule by considering the frequency. Journal of the Korean Data & Information Science Society, 21, 1061-1069.
12 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.
13 Liu, B., Hsu, W., Chen, S. and Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15, 47-55.   DOI   ScienceOn
14 Mortimer, H. (1988), The logic of induction, Prentice Hall, Paramus.
15 Nozick, R. (1981), Philosophical explanations, Clarendon Press, Oxford.
16 Park, H. C. (2011a). Proposition of negatively pure association rule threshold. Journal of the Korean Data & Information Science Society, 22, 179-188.
17 Park, H. C. (2011b). The proposition of attributably pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 22, 235-243.
18 Park, H. C. (2011c). The application of some similarity measures to association rule thresholds. Journal of the Korean Data Analysis Society, 13, 1331-1342.
19 Park, H. C. (2012a). Negatively attributable and pure confidence for generation of negative association rules. Journal of the Korean Data & Information Science Society, 14, 707-716.
20 Park, H. C. (2012b). Exploration of PIM based similarity measures as association rule thresholds. Journal of the Korean Data & Information Science Society, 23, 1127-1135.   DOI   ScienceOn
21 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.
22 Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. Proceedings of the 9th National Conference on Artificial Intelligence: Knowledge Discovery in Databases, 229-248.
23 Rips, L. J. (2001). Two kinds of reasoning. Psychological Science, 12, 129-134.   DOI   ScienceOn
24 Tan, P. N., Kumar, V. and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 32-41.
25 Saygin Y., Vassilios S. V. and Clifton C.(2002). Using unknowns to prevent discovery of association rules. Proceedings of 2002 Conference on Research Issues in Data Engineering, 45-54.
26 Silberschatz, A. and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge Data Engineering, 8, 970-974.   DOI   ScienceOn
27 Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. Proceedings of the 21st VLDB Conference, 407-419.