Mining action rules from scratch

Expert Systems with Applications

Volumn 29, Issue 3, 2005, Pages 691-699

  He, Zengyou ^a   Xu, Xiaofei ^a   Deng, Shengchun ^a   Ma, Ronghua ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b NANJING INSTITUTE OF GEOGRAPHY AND LIMNOLOGY   (China)

Author keywords

Action rule; Association rules; CRM; Data mining; Flexible attributes; Negative example; Positive example; Stable attributes

Indexed keywords

ALGORITHMS; DATA PROCESSING; DECISION THEORY; EXPERT SYSTEMS; LEARNING SYSTEMS;

ACTION RULES; ASSOCIATION RULES; CRM; FLEXIBLE ATTRIBUTES; NEGATIVE EXAMPLES; POSITIVE EXAMPLES; STABLE ATTRIBUTES;

DATA MINING;

EID: 24144432823     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.04.031     Document Type: Article

References (17)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In: Proceedings of VLDB'94 (pp. 478-499).
2. Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K., & Wets, G. (2000). A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. In: Proceedings of KDD'00 (pp. 300-304).
3. J. Cleinberg, C. Papadimitriou, and P. Raghavan A microeconomic view of data mining Data Mining and Knowledge Discovery 2 4 1998 311 324
4. Y. Elovici, and D. Braha A decision-theoretic approach to data mining IEEE Transactions on Systems, Man and Cybernetics, Part A 33 1 2003 42 51
5. Li, W., Han, J., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of ICDM'01 (pp. 369-376).
6. Lin, T. Y., Yao, Y. Y., & Louie, E. (2002). Mining value added association rules. In: Proceedings of PAKDD'02 (pp. 328-333).
7. Ling, C. X., Chen, T., Yang, Q., & Chen, J. (2002). Mining optimal actions for intelligent CRM. In: Proceedings of ICDM'02 (pp. 767-770).
8. Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. In: Proceedings of KDD'98 (pp. 80-86).
9. Liu, B., Hsu, W., & Ma, Y. (2001). Identifying non-actionable association rules. In: Proceedings of KDD'01 (pp. 329-334).
10. Padmanabhan, B., & Tuzhilin, A. (1998). A belief-driven method for discovering unexpected patterns. In: Proceedings of KDD'98 (pp. 94-100).
11. J.R. Quinlan C4.5: Programs for machine learning 1993 Morgan Kaufmann Publishers Los Altos, CA
12. Ras, Z. W., & Tsay, L. -S. (2003). Discovering extended action-rules, System DEAR. In: Proceedings of the IIS'2003 symposium (pp. 293-300).
13. Ras, Z. W., & Wieczorkowska, A. (2000). Action-rules: How to increase profit of a company. In: Proceedings of PKDD'00 (pp. 587-592).
14. Wang, K., Su, M. Y. T. (2002). Item selection by 'hub-authority' profit ranking. In: Proceedings of KDD'02 (pp. 652-657).
15. Wang, K., Zhou, S., & Han, J. (2002). Profit mining: From patterns to actions. In: Proceedings of EDBT'02 (pp. 70-87).
16. Yang, Q., & Cheng, H. (2002). Mining case bases for action recommendation. In: Proceedings of ICDM'02 (pp. 522-529).
17. Yang, Q., Yin, J., Lin, C. X., & Chen, T. (2003). Postprocessing decision tress to extract actionable knowledge. In: Proceedings of ICDM'03.