Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Weighted association rules considering item RFM scores

항목 알에프엠 점수를 고려한 가중 연관성 규칙

  • Received : 2010.09.15
  • Accepted : 2010.10.31
  • Published : 2010.11.30
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Abstract

One of the important goals in data mining is to discover and decide the relationships between different variables. Association rules are required for this technique and it find meaningful rules by quantifying the relationship between two items based on association measures such as support, confidence, and lift. In this paper, we presented the evaluation criteria of weighted association rule considering item RFM scores as importance of items. Original RFM technique has been used most widely applied method using customer information to find the most profitable customers. And then we compared general association rule technique with weighted association rule technique through the simulation data.

데이터 마이닝의 중요 목표 중의 하나는 여러 변수들 간의 관계를 발견하고 결정하는 것이다. 이를 위해 필요한 기법인 연관성 규칙은 각 항목들 간의 관련성을 찾아내는 데 활용되며, 지지도, 신뢰도, 향상도 등의 연관성 측도를 기반으로 두 항목간의 관계를 수치화함으로써 의미 있는 규칙을 찾아 낸다. 본 논문에서는 수익성이 가장 높은 고객을 찾기 위해 고객 정보를 이용하는 기법으로 가장 널리 사용되어온 방법인 알에프엠 기법을 항목에 적용하여 항목의 알에프엠 점수를 항목의 중요도로 고려하여 가중 연관성 규칙의 평가기준을 제시하였다. 모의실험에서는 일반적인 연관성 규칙과 알에프엠 점수를 가중치로 한 가중 연관성 규칙의 유용성을 비교하였다.

Keywords

References

  1. 오윤경, 김지경, 김상훈 (2003). 고객정보의 종류와 양이 구매모형 예측력에 미치는 영향에 관한 연구. <경영논집>, 37, 91-121.
  2. 이정숙, 김재련 (2003). 항목별 최소지지도와 가중 항목을 고려한 연관규칙. <한국산업경영시스템학회 2003 추계학술대회논문집>, 31-35.
  3. 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.
  4. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, 487-499.
  5. Bayardo, R. J. (1998). Efficiently mining long patterns from databases. Proc. of ACM SIGMOD Conference on Management of Data, 85-93.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11, 68-77.
  11. 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.
  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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. Proceedings of the 21st VLDB Conference, 407-419.
  20. Toivonen, H. (1996). Sampling large database for association rules. Proceedings of the 22nd VLDB Conference, 134-145.