References
- 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.
- 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.
- Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proceedings of ACM SIGMOD Conference on Management of Data, 85-93.
- Berzal, F., Cubero, J. C., Marin, N. and Sanchez, D. (2004). Building multi-way decision trees with numerical attributes. Information Sciences, 165, 73-90. https://doi.org/10.1016/j.ins.2003.09.018
- 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.
- 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.
- 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.
- Freitas, A. (1999). On rule interestingness measures. Knowledge-based System, 12, 309-315. https://doi.org/10.1016/S0950-7051(99)00019-2
- Han, J. and Fu, Y. (1995). Discovery of multiple-level association rules from large databases. Proceeding of the 21st VLDB Conference, 420-431.
- 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.
- 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.
- 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.
- Kuo, Y. T. (2009) Mining surprising patterns, The doctoral paper of Melbourne university, Australia.
- Liu, B., Hsu, W., Chen, S. and Ma, Y. (2000). Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15, 47-55. https://doi.org/10.1109/5254.889106
- 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.
- Park, H. C. (2010a). Standardization for basic association measures in association rule mining. Journal of the Korean Data & Information Science Society, 21, 891-899.
- Park, H. C. (2010b). Weighted association rules considering item RFM scores. Journal of the Korean Data & Information Science Society, 21, 1147-1154.
- Park, H. C. (2011a). Proposition of negatively pure association rule threshold. Journal of the Korean Data & Information Science Society, 22, 179-188.
- 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.
- Park, H. C. (2011c). The application of some similarity measures to association rule thresholds. Journal of the Korean Data Analysis Society, 13, 1331-1342.
- 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.
- 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.
- 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.
- Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press, 229-248.
- Silberschatz, A. and Tuzhilin, A. (1996) What makes patterns interesting in knowledge discovery systems. IEEE transactions on Knowledge Data Engineering, 8, 970-974. https://doi.org/10.1109/69.553165
- 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.
- 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.
- Toivonen, H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.
Cited by
- Signed Hellinger measure for directional association vol.27, pp.2, 2016, https://doi.org/10.7465/jkdi.2016.27.2.353
- The development of symmetrically and attributably pure confidence in association rule mining vol.25, pp.3, 2014, https://doi.org/10.7465/jkdi.2014.25.3.601
- Proposition of balanced comparative confidence considering all available diagnostic tools vol.26, pp.3, 2015, https://doi.org/10.7465/jkdi.2015.26.3.611