Mining the data from a hyperheuristic approach using associative classification

Expert Systems with Applications

Volumn 34, Issue 2, 2008, Pages 1093-1101

  Thabtah, Fadi ^a   Cowling, Peter ^b  

^a UNIVERSITY OF HUDDERSFIELD   (United Kingdom)

^b UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Associative classification; Classification; Data mining; Hyperheuristic; Scheduling

Indexed keywords

ASSOCIATION RULES; DATABASE SYSTEMS; DECISION TREES; HEURISTIC ALGORITHMS; PROBLEM SOLVING; SCHEDULING;

ASSOCIATIVE CLASSIFICATION; DATA SETS; LOW-LEVEL HEURISTICS; PERSONNEL SCHEDULING PROBLEM;

DATA MINING;

EID: 36148983079     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2006.12.018     Document Type: Article

References (27)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rule. In Proceedings of the 20th international conference on very large data bases (pp. 487-499).
2. Aicklen U., and Dowsland K. Exploiting problem structure in a genetic approach to a nurse rostering problem. Journal of Scheduling 3 (2000) 139-153
3. Ali, K., Manganaris, S., & Srikant, R. (1997). Partial classification using association rules. In D. Heckerman, H. Mannila, D. Pregibon, & R. Uthurusamy (Eds.), Proceedings of the third international conference on knowledge discovery and data mining (pp. 115-118).
4. Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimisation: overview and conceptual comparison. ACM Computing Surveys (CSUR) (pp. 268-308).
5. CBA (1998). .
6. Cendrowska J. PRISM: an algorithm for inducing modular rules. International Journal of Man-Machine Studies 27 4 (1987) 349-370
7. Cohen W. Fast effective rule induction. Proceedings of the 12th international conference on machine learning (1995), Morgan Kaufmann, CA 115-123
8. Cowling, P., & Chakhlevitch, K. (2003). Hyperheuristics for managing a large collection of low level heuristics to schedule personnel. In Proceedings of IEEE conference on evolutionary computation (pp. 1214-1221). Canberra, Australia.
9. Cowling, P., Kendall, G., & Han, L. (2002) An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem. In Proceedings of congress on evolutionary computation (CEC 2002) (pp. 1185-1190). Hilton Hawaiian Village Hotel, Honolulu, Hawaii, May 12-17, ISBN 0-7803-7282-4.
10. Cowling P., Kendall G., and Soubeiga E. A hyperheuristic approach to scheduling a sales summit. Proceedings of the third international conference of practice and theory of automated timetabling (PATAT 2000). LNCS vol. 2079 (2000), Springer 176-190
11. Fayyad, U., & Irani, K. (1993). Multi-interval discretisation of continues-valued attributes for classification learning. In Proceedings ofI JCAI (pp. 1022-1027).
12. Frank E., and Witten I. Generating accurate rule sets without global optimisation. Proceedings of the fifteenth international conference on machine learning (1998), Morgan Kaufmann, Madison, Wisconsin 144-151
13. Furnkranz J., and Widmer G. Incremental reduced error pruning. Machine learning: Proceedings of the 11th annual conference (1994), Morgan Kaufmann, New Brunswick, New Jersey
14. Hamiez J., and Hao J. Solving the sports league scheduling problem with Tabu search. Lecture notes in artificial intelligence vol. 2148 (2001), Springer 24-36
15. Hansen P., and Mladenovic N. Variable neighbourhood search. Computer and Operations Research (1997) 1097-1100
16. Li W., Han J., and Pei J. CMAR: accurate and efficient classification based on multiple-class association rule. Proceedings of the ICDM'01 (2001), San Jose, CA 369-376
17. Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the KDD (pp. 80-86). New York, NY.
18. McAloon, K., TretKoff, C., & Wetzel, G., 1997. Sports league scheduling. In Proceedings of the third ILOG optimisation suite international users conference. Paris, France, 1997.
19. Merz C., and Murphy P. UCI repository of machine learning databases (1996), University of California, Department of Information and Computer Science, Irvine, CA
20. Quinlan J. Discovering rules from large collections of examples: a case study. In: Michie D. (Ed). Expert systems in the micro-electronic age (1979), Edinburgh University Press, Edinburgh 168-201
21. Quinlan J. Generating production rules from decision trees. Proceedings of the 10th international joint conferences on artificial intelligence (1987), Morgan Kaufmann, CA 304-307
22. Quinlan J. C4.5: Programs for machine learning (1993), Morgan Kaufman, San Mateo, CA
23. Thabtah, F., Cowling, P., & Peng, Y. (2004). MMAC: a new multi-class, multi-label associative classification approach. In Proceedings of the fourth IEEE international conference on data mining (ICDM '04) (pp. 217-224) Brighton, UK. (Nominated for the Best paper award).
24. Thabtah, F., Cowling, P., & Peng, Y. (2005). MCAR: multi-class classification based on association rule approach. In Proceeding of the 3rd IEEE international conference on computer systems and applications (pp. 1-7). Cairo, Egypt.
25. WEKA (2000): Data Mining Software in Java: .
26. Witten, I., & Frank, E. (2000). Data mining: practical machine learning tools and techniques association rules. In Proceedings of the 3rd KDD conference (pp. 283-286).
27. Yin, X., & Han, J. (2003) CPAR: classification based on predictive association rule. In Proceedings of the SDM (pp. 369-376). San Francisco, CA.