Rule pruning in associative classification mining

Internet and Information Technology in Modern Organizations: Challenges and Answers - Proceedings of the 5th International Business Information Management Association Conference, IBIMA 2005

Volumn 1, Issue , 2005, Pages

  Thabtah, Fadi ^a  

^a UNIVERSITY OF HUDDERSFIELD   (United Kingdom)

Author keywords

Association rule; Associative classification; Data mining; Pruning methods

Indexed keywords

ASSOCIATION RULES; DATA MINING; INFORMATION MANAGEMENT; INFORMATION TECHNOLOGY;

ASSOCIATION RULE DISCOVERY; ASSOCIATIVE CLASSIFICATION; CLASSIFICATION SYSTEM; DATA COLLECTION; DATA MINING APPLICATIONS; DATA MINING TASKS; PRUNING ALGORITHMS; PRUNING METHODS;

CLASSIFICATION (OF INFORMATION);

EID: 84903642278     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Agrawal, R., and Srikant, R. (1994). Fast algorithms for mining association rule. Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499).
2. Ali, K., Manganaris, S., and Srikant, R. (1997). Partial classification using association rules. In Heckerman, D., Mannila, H., Pregibon, D., and Uthurusamy, R., (eds.). Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, (pp. 115-118.).
3. Antonie, M., Zaïane, O., and Coman, A. (2003). associative classifiers for medical images. Lecture Notes in Artificial Intelligence 2797, Mining Multimedia and Complex Data, (pp. 68-83). Springer-Verlag.
4. Baralis, E., and Torino, P. (2000). A lazy approach to pruning classification rules. Proceedings of the 2002 IEEE ICDM'02, (pp. 35).
5. Clark, P., and Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Y. Kodratoff, editor, Machine Learning-EWSL-91, (pp. 151-163). Berlin, 1991, Springer-Verlag.
6. Dong, G., and Li., J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. Proceedings of the Int'l Conf. Of Knowledge Discovery and Data Mining, (pp. 43-52).
7. Li, W., Han, J., and Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple-class association rule. Proceedings of the ICDM'01 (pp. 369-376). San Jose, CA.
8. Liu, B., Hsu, W., and Ma, Y. (1999). Mining association rules with multiple minimum supports. Proceedings of the fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp.337-341). San Diego, California.
9. Liu, B., Hsu, W., and Ma, Y. (1998). Integrating classification and association rule mining. Proceedings of the KDD, (pp. 80-86). New York, NY.
10. Merz, C., and Murphy, P. (1996). UCI repository of machine learning databases. Irvine, CA, University of California, Department of Information and Computer Science.
11. Quinlan, J. (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
12. Quinlan, J. (1987). Simplifying decision trees. International journal of man-machine studies, 27-(3), 221-248.
13. Snedecor, W., and Cochran, W. (1989). Statistical Methods, Eighth Edition, Iowa State University Press.
14. Thabtah, F., Cowling, P., and Peng, Y. (2005f) MCAR: Multi-class classification based on association rule approach. Proceeding of the 3rd IEEE International Conference on Computer Systems and Applications (pp. 1-7). Cairo, Egypt.
15. Thabtah, F., Cowling, P., and Peng, Y. (2004a) MMAC: A new multi-class, multi-label associative classification approach. Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM '04), (pp. 217-224). Brighton, UK. (Nominated for the Best paper award).
16. Wang, K., Zhou, S., and He, Y. (2000). Growing decision tree on support-less association rules. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 265-269). Boston, Massachusetts.
17. Witten, I., and Frank, E. (2000). Data mining:practical machine learning tools and techniques with Java implementations. San Francisco: Morgan Kaufmann.
18. Yin, X., and Han, J. (2003). CPAR: Classification based on predictive association rule. Proceedings of the SDM (pp. 369-376). San Francisco, CA.
19. WEKA (2000): Mining software in Java http://www.cs.waikato.ac.nz/ml/weka.