References
- 오윤경, 김지경, 김상훈 (2003). 고객정보의 종류와 양이 구매모형 예측력에 미치는 영향에 관한 연구. <경영논집>, 37, 91-121.
- 이정숙, 김재련 (2003). 항목별 최소지지도와 가중 항목을 고려한 연관규칙. <한국산업경영시스템학회 2003 추계학술대회논문집>, 31-35.
- 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.
- Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487-499.
- Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proc. of ACM SIGMOD Conference on Management of Data, 85-93.
- 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. (2007). Association rule mining by environmental data fusion. Journal of the Korean Data & Information Science Society, 18, 279-287.
- Cho, K. H. and Park, H. C. (2008). A study of association rule application using self-organizing map for fused data. Journal of the Korean Data & Information Science Society, 19, 95-104.
- 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.
- Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11, 68-77.
- 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.
- Kim, J., Ceong, H. and Won, Y. (2002). Weighted association rule mining for item groups with different properties and risk assessment for networked systems. IEICE Transaction on Information and Systems, 85, 1-7.
- Lee, S., Choi, S., Kim, K. and Kang, C. (2004). Study on development the optimal RFM model for customer segmentation. Journal of the Korean Data Analysis SocietyI , 6, 1829-1840.
- 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.
- Park, H. C. (2008). The proposition of conditionally pure confidence in association rule mining. Journal of the Korean Data & Information Science Society, 19, 1141-1151.
- Park J. S., Chen M. S. and Philip S. Y. (1995). An effective hash-based algorithms for mining association rules. Proceedings of ACM SIGMOD Conference on Management of Data, 175-186.
- 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 itemsets. Proceedings of ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 21-30.
- Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. Proceedings of the 21st VLDB Conference, 407-419.
- Toivonen, H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.