Clustering interestingness measures with positive correlation

ICEIS 2005 - Proceedings of the 7th International Conference on Enterprise Information Systems

Volumn , Issue , 2005, Pages 248-253

  Huynh, Xuan Hiep ^a   Guillet, Fabrice ^a   Briand, Henri ^a  

^a UNIVERSITÉ DE NANTES   (France)

Author keywords

Cluster; Intensity of implication; Interestingness measure; Interestingness property; Objective measure

Indexed keywords

CLUSTER; INTENSITY OF IMPLICATION; INTERESTINGNESS; INTERESTINGNESS MEASURES; OBJECTIVE MEASURE;

ASSOCIATION RULES;

INFORMATION SYSTEMS;

EID: 33746220849     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Agrawal, R., Imielinski, T., and Swami, A. (1993). Mining association rules between sets of items in large data-bases. In Proceedings of 1993 ACM-SIGMOD International Conference on Management of Data.
2. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference.
3. Bayardo, J. and Agrawal, R. (1999). Mining the most interestingness rules. In Proceedings of the Fifth ACM SIGKDD International Confeference On Knowledge Discovery and Data Mining.
4. Blake, C. and Merz, C. (1998). UCI Repository of machine learning databases, http://www.ics.uci.edu/-mlearn/MLRepository.html. University of California, Irvine, Dept. of Information and Computer Sciences.
5. Blanchard, J., Kuntz, P., Guillet, F., and Gras, R. (2003). Implication intensity: from the basic statistical definition to the entropic version. In Statistical Data Mining and Knowledge Discovery. Chapman&Hall, CRC Press.
6. Brin, S., Motwani, R., and Silverstein, C. (1997). Beyond market baskets: Generalizing association rules to correlation. In Proceedings of ACM SIGMOD Conference.
7. Freitas, A. (1999). On rule interestingness measures. In Knowledge-Based Systems, 12(5-6).
8. Gavrilov, M., Anguelov, D., Indyk, P., and Motwani, R. (2000). Mining the stock market: which measure is best? In Proceedings of the Sixth International Conference on Knowledge Discovery and Data Mining.
9. Gras, R. (1996). L'implication statistique - Nouvelle méthode exploratoire de données. La pensée sauvage édition.
10. Hilderman, R. and Hamilton, H. (2001). Knowledge Discovery and Measures of Interestingness. Kluwer Academic Publishers.
11. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkano, A. I. (1994). Finding interesting rules from larges sets of discovered association rules. In the Third International Conference on Information and Knowledge Management. ACM Press.
12. Kononenco, I. (1995). On biases in estimating multi-valued attributes. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI' 95).
13. Liu, B., Hsu, W., Mun, L., and Lee, H. (1999). Finding interestingness patterns using user expectations. In IEEE Transactions on Knowledge and Data Mining 11(1999).
14. Padmanabhan, B. and Tuzhilin, A. (1998). A beliefdriven method for discovering unexpected patterns. In Proceedings of the 4th international conference on Knowledge Discovery and Data Mining.
15. Piatetsky-Shapiro, G. (1991). Discovery, analysis and presentation of strong rules. In Knowledge Discovery in Databases. MIT Press.
16. Saporta, G. (1990). Probabilités, Analyse des Données et Statistique. Editions Technip, Paris.
17. Silberschartz, A. and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. In IEEE Transaction on Knowledge and Data Engineering (Vol. 8, No. 9).
18. Tan, P., Kumar, V., and Srivastava, J. (2004). Selecting the right objective measure for association analysis. In Information Systems 29(4).