Discovering interesting association rules by clustering

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3339, Issue , 2004, Pages 1055-1061

  Zhao, Yanchang ^a   Zhang, Chengqi ^a   Zhang, Shichao ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Association Rules; Clustering; Interestingness

Indexed keywords

ALGORITHMS; DATA MINING; DATA REDUCTION; DATABASE SYSTEMS; KNOWLEDGE ACQUISITION;

ASSOCIATION RULES; CLUSTERING; COLLECTIVE STRENGTH; INTERESTINGNESS;

KNOWLEDGE BASED SYSTEMS;

EID: 22944490533     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30549-1_101     Document Type: Conference Paper

References (11)

1. R. Agrawal, T. Imielinski and A. Swami: Mining association rules between sets of tiems in large databases. In Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD'93), Washington, D.C., USA, May 1993, pp. 207-216.
2. R. Chan, Q. Yang, and Y.-D. Shen: Mining high utility itemsets. In Proc. of the 2003 IEEE International Conference on Data Mining, Florida, USA, November 2003.
3. G. Dong and J. Li: Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In Proc. of the 2nd Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Melbourne, Australia, April 1998, pp. 72-86.
4. Erica Kolatch: Clustering Algorithms for Spatial Databases: A Survey. Dept. of Computer Science, University of Maryland, College Park, 2001. http://citeseer.nj.nec.com/436843.html
5. U.M. Fayyad, G. Piatetsky-Shapiro and P. Smyth: From data mining to knowledge discovery: an overview. In Advances in Knowledge Discovery & Data Mining, pp. 1-34, AAAI/MIT, 1996.
6. Alex A. Freitas: On objective measures of rule surprisingness. In Proc. of 2nd European Symp PKDD'98, Nantes, France, 1998, pp. 1-9.
7. Robert J. Hilderman and Howard J. Hamilton: Knowledge discovery and interestingness measures: a survey, Tech. Report 99-4, Department of Computer Science, University of Regina, October 1999.
8. Won-Young Kim, Young-Koo Lee, and Jiawei Han: CCMine: efficient mining of confidence-closed correlated patterns. In Proc. of 2004 Pacific-Asia Conf. on Knowledge Discovery and Data Mining, Sydney, Australia, May 2004, pp.569-579.
9. B. Liu, W. Hsu, S. Chen, and Y. MA: Analyzing the subjective interestingness of association rules. IEEE Intelligent Systems, 15(5):47-55, 2000.
10. E. Omiecinski: Alternative interest measures for mining associations. IEEE Trans. Knowledge and Data Engineering, 15:57-69, 2003.
11. Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava: Selecting the right interestingness measure for association patterns. In Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, Alberta, 2002, pp. 32-41.