Browse > Article
http://dx.doi.org/10.7465/jkdi.2013.24.3.523

The proposition of compared and attributably pure confidence in association rule mining  

Park, Hee Chang (Department of Statistics, Changwon National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.3, 2013 , pp. 523-532 More about this Journal
Abstract
Generally, data mining is the process of analyzing big data from different perspectives and summarizing it into useful information. The most widely used data mining technique is to generate association rules, and it finds the relevance between two items in a huge database. This technique has been used to find the relationship between each set of items based on the interestingness measures such as support, confidence, lift, etc. Among many interestingness measures, confidence is the most frequently used, but it has the drawback that it can not determine the direction of the association. The attributably pure confidence and compared confidence are able to determine the direction of the association, but their ranges are not [-1, +1]. So we can not interpret the degree of association operationally by their values. This paper propose a compared and attributably pure confidence to compensate for this drawback, and then describe some properties for a proposed measure. The comparative studies with confidence, compared confidence, attributably pure confidence, and a proposed measure are shown by numerical example. The results show that the a compared and attributably pure confidence is better than any other confidences.
Keywords
Association threshold; attributably pure confidence; compared and attributably pure confidence; compared confidence; confidence;
Citations & Related Records
Times Cited By KSCI : 4  (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 Ahn, K. and Kim, S. (2003). A new interstingness measure in association rules mining. Journal of the Korean Institute of Industrial Engineers, 29, 41-48.
3 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
4 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, 337-341.
5 Park, H. C. (2010a). Standardization for basic association measures in association rule mining. Journal of the Korean Data & Information Science Society, 21, 891-899.   과학기술학회마을
6 Park, H. C. (2010b). Weighted association rules considering item RFM scores. Journal of the Korean Data & Information Science Society, 21, 1147-1154.   과학기술학회마을
7 Toivonen, H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.
8 Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proceedings of ACM SIGMOD Conference on Management of Data, 85-93.
9 Berzal, F., Cubero, J. C., Marin, N. and Sanchez, D. (2004). Building multi-way decision trees with numerical attributes. Information Sciences, 165, 73-90.   DOI   ScienceOn
10 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.
11 Park, H. C. (2012). Exploration of symmetric similarity measures by conditional probabilities as association rule thresholds. Journal of the Korean Data Analysis Society, 14, 707-716.
12 Park, H. C. (2011a). Proposition of negatively pure association rule threshold. Journal of the Korean Data & Information Science Society, 22, 179-188.   과학기술학회마을
13 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.   과학기술학회마을
14 Park, H. C. (2011c). The application of some similarity measures to association rule thresholds. Journal of the Korean Data Analysis Society, 13, 1331-1342.
15 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.
16 Pei, J., Han, J. and Mao, R. (2000). CLOSET: an efficient algorithm for mining frequent closed item-sets. Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 21-30.
17 Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press, 229-248.
18 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
19 Srinkant, R., Vu, Q. and Agrawal, R. (1997). Mining association rules with item constraints. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, 67-73.
20 Cho, K. H. and Park, H. C. (2011a). Study on the multi intervening relation in association rules. Journal of the Korean Data Analysis Society, 13, 297-306.
21 Cho, K. H. and Park, H. C. (2011b). Discovery of insignificant association rule s using external variable. Journal of the Korean Data Analysis Society, 13, 1343-1352.
22 Freitas, A. (1999). On rule interestingness measures. Knowledge-based System, 12, 309-315.   DOI   ScienceOn
23 Han, J. and Fu, Y. (1995). Discovery of multiple-level association rules from large databases. Proceeding of the 21st VLDB Conference, 420-431.
24 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.
25 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.
26 Jin, D. S., Kang, C., Kim, K. K. and Choi, S. B. (2011). CRM on travel agency using association rules. Journal of the Korean Data Analysis Society, 13, 2945-2952.
27 Kuo, Y. T. (2009) Mining surprising patterns, The doctoral paper of Melbourne university, Australia.
28 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.