Abstract
Using a single confidence threshold will result in a dilemmatic situation when simultaneously studying positive and negative association rule (PNAR), i.e., the forms A⇒B, A⇒ ¬B, ¬A⇒B and ¬A⇒ ¬B. A method based on four confidence thresholds for the four forms of PNARs is proposed. The relationships among the four confidences, which show the necessity of using multiple confidence thresholds, are also discussed. In addition, the chi-squared test can avoid generating misleading rules that maybe occur when simultaneously studying the PNARs. The method of how to apply chi-squared test in mining association rules is discussed. An algorithm PNARMC based on the chi-squared test and the four confidence thresholds is proposed. The experimental results demonstrate that the algorithm can not only generate PNARs rightly, but can also control the total number of rules flexibly.
This work was supported by the National Nature Science Foundation of China (NNSFC) under the grant 60271015.
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References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large database. In: Proceeding of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM Press, New York (1993)
Wu, X., Zhang, C., Zhang, S.: Mining both positive and negative association rules. In: Proceedings of the 19th International Conference on Machine Learning (ICML-2002), pp. 658–665. Morgan Kaufmann Publishers, San Francisco (2002)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market: Generalizing Association Rules to Correlations. In: Processing of the ACM SIGMOD Conference, pp. 265–276 (1997)
Li, X., Liu, Y., Peng, J.: The extended association rules and atom association rules. Journal of Computer Research and Application, 1740–1750 (December 2002)
Savasere, A., Omiecinski, E., Navathe, S.: Mining for Strong Negative Associations in a Large Database of Customer Transaction. In: Proceedings of the 1998 International Conference on Data Engineering, pp. 494–502 (1998)
Zhang, C., Zhang, S. (eds.): Association Rule Mining. LNCS (LNAI), vol. 2307, pp. 47–84. Springer, Heidelberg (2002)
Wu, X., Zhang, C., Zhang, S.: Efficient Mining of Both Positive and Negative Association Rules. ACM Transactions on Information Systems 22(3), 381–405 (2004)
Boulicaut, J.-F., Bykowski, A., Jeudy, B.: Towards the Tractable Discovery of Association Rules with Negations. In: Proceedings of the Fourth International Conference on Flexible Query Answering Systems FQAS 2000, Warsaw (PL), pp. 425–434 (2000)
Liu, B., Hsu, W., Ma, Y.: Mining Association Rules with Multiple Minimum Supports. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Diego, CA, USA (1999)
Tan, P., Kumar, V.: Interestingness measures for association patterns: a perspective. In: KDD-2000 Workshop on Post-processing in Machine Learning and Data Mining (2000)
Tan, P.-N., Kumar, V., Srivastava, J.: Selecting the Right Interestingness Measure for Association Patterns. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton (CA), pp. 32–41 (2002)
Silverstein, C., Brin, S., Motwani, R.: Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery 2(1), 39–68 (1998)
Liu, B., Hsu, W., Ma, Y.: Identifying Non-Actionable Association Rules. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, San Francisco, CA, pp. 329–334 (2001)
Hilderman, R.J., Hamilton, H.J.: Applying Objective Interestingness Measures in Data Mining Systems. In: Zighed, A.D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 432–439. Springer, Heidelberg (2000)
Sergio, A.A.: Chi-squared computation for association rules: preliminary results. Technical Report BC-CS-2003-01 July 2003, Computer Science Dept. Boston College Chestnut Hill, MA 02467 USA (2003)
Dong, X., Wang, S., Song, H., Lu, Y.: Study on Negative Association Rules. Transactions of Beijing Institute of Technology, China, pp. 978–981 (November 2004)
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Dong, X., Sun, F., Han, X., Hou, R. (2006). Study of Positive and Negative Association Rules Based on Multi-confidence and Chi-Squared Test. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_10
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DOI: https://doi.org/10.1007/11811305_10
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