1 |
Park, H. C. (2010a). Weighted association rules considering item RFM scores. Journal of the Korean Data & Information Science Society, 21, 1147-1154.
|
2 |
Park, H. C. (2010b). Standardization for basic association measures in association rule mining. Journal of the Korean Data & Information Science Society, 21, 891-899.
|
3 |
Park, H. C. (2011a). Proposition of negatively pure association rule threshold. Journal of the Korean Data & Information Science Society, 22, 179-188.
|
4 |
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.
|
5 |
Park, H. C. (2011c). The application of some similarity measures to association rule thresholds. Journal of the Korean Data Analysis Society, 13, 1331-1342.
|
6 |
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.
|
7 |
Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. Knowledge Discovery in Databases, AAAI/MIT Press, 229-248.
|
8 |
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.
|
9 |
Shang, S., Dong, X., Geng, R. and Zhao, L. (2008). Mining negative association rules in multi-database. Proceedings of the Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 596- 599.
|
10 |
Sim, A., Indrawan, M. and Srinivasan, B. (2008). The importance of negative associations and the discovery of association rule pairs. International Journal of Business Intelligence and Data Mining, 3, 158-176.
DOI
|
11 |
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.
|
12 |
Bala, P. K. (2009). A technique for mining negative association rules. Proceedings of the 2nd Bangalore Annual Compute Conference, 23-23.
|
13 |
Cho, K. H. and Park, H. C. (2011). Discovery of insignificant association rule s using external variable. Journal of the Korean Data Analysis Society, 13, 1343-1352.
|
14 |
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.
|
15 |
Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11, 68-77.
|
16 |
Han, J. and Kamber, M. (2006). Data mining : Concepts and techniques, Morgam Kaufmann, USA.
|
17 |
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.
|
18 |
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.
|
19 |
Hwang, J. and Kim, J. (2003). Target marketing using inverse association rule. Journal of Intelligence and Information Systems, 9, 195-209.
|
20 |
Lee, J., Park, S., Kang, Y., Park, S. and Lee, J. (2003). Finding negative association rules with Boolean analyzer. Proceedings of the Korean Institute of Information Scientists and Engineers, 30, 187-189.
|
21 |
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.
|