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Combining evolutionary algorithmsand exact approaches for multi-objective knowledge discovery

Published online by Cambridge University Press:  21 February 2008

Mohammed Khabzaoui ,
Clarisse Dhaenens  and
El-Ghazali Talbi
Mohammed Khabzaoui
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; [email protected]; [email protected]
Clarisse Dhaenens
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; [email protected]; [email protected]
El-Ghazali Talbi
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; [email protected]; [email protected]
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Abstract

An important task of knowledge discovery deals with discovering association rules. This very general model has been widely studied and efficient algorithms have been proposed. But most of the time, only frequent rules are seeked. Here we propose to consider this problem as a multi-objective combinatorial optimization problem in order to be able to also find non frequent but interesting rules. As the search space may be very large, a discussion about different approaches is proposed and a hybrid approach that combines a metaheuristic and an exact operator is presented.

Keywords

Hybridizationmulti-objective optimizationknowledge discoveryassociation rules
Type
Research Article
Information
RAIRO - Operations Research , Volume 42 , Issue 1: Cooperative methods for multiobjective optimization , January 2008 , pp. 69 - 83
Copyright
© EDP Sciences, ROADEF, SMAI, 2008

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References

R. Agrawal and R. Srikant, Fast algorithms for mining association rules, in Proc. 20th Int. Conf. Very Large Data Bases, VLDB, edited by J.B. Bocca, M. Jarke, and C. Zaniolo, Morgan Kaufmann 12 (1994) 487–499
D.L.A. Araujo, H.S. Lopes and A.A. Freitas, A Parallel Genetic Algorithm for Rule Discovery in Large Databases, in Proc. 1999 IEEE Systems, Man and Cybernetics Conf., Vol. III (1999) 940–945, Tokyo, Japan.
M. Basseur, F. Seynhaeve and E-G. Talbi, Adaptive mechanisms for multi-objective evolutionary algorithms. IMACS multiconference, Computational Engineering in Systems Applications (CESA'03), IEEE Service Center, Piscataway, New Jersey, S3-R-00-222:100–107 (2003).
C. Borgelt, Efficient implementations of apriori and eclat, in Workshop Frequent Item Set Mining Implementations (FIMI 2003, Melbourne, FL, USA) 90 (2003).
C.A. Coello, D.A. Van Veldhuizen and G.B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers (2002).
Cotta, C. and Troya, J.M., Embedding branch and bound within evolutionary algorithms. Appl. Intell. 18 (2003) 137–153 CrossRef
Fonseca, C.M. and Fleming, P.J., An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Comput. 3 (1995) 1–16. CrossRef
Freitas, A., On rule interestingness measures. Knowledge-Based Syst. J. 12 (1999) 309–315. CrossRef
R. Hilderman and H. Hamilton, Knowledge discovery and interestingness measures: A survey, technical report cs 99-04. Technical report, Department of Computer Science, University of Regina, October (1999).
Hong, T.P., Wang, H. and Chen, W., Simultaneously applying multiple mutation operators in genetic algorithms. J. Heuristics 6 (2000) 439–455. CrossRef
A. Jaszkiewicz, On the performance of multiple objective genetic local search on the 0/1 knapsack problem. a comparative experiment. Technical Report RA-002/2000, Institute of Computing Science, Poznan University of Technology, Poznan, Poland (2000).
M. Khabzaoui, C. Dhaenens, A. N'Guessan and E.-G. Talbi, Etude exploratoire des critères de qualité des règles d'association en datamining, in Journées Françaises de Statistique (2003) 583–587.
M. Khabzaoui, C. Dhaenens and E.-G. Talbi, Association rules discovery for DNA microarray data. Bioinformatics Workshop of SIAM International Conference on Data Mining (2004) 63–71.
M. Khabzaoui, C. Dhaenens and E.-G. Talbi, A Multicriteria Genetic Algorithm to analyze DNA microarray data, in Congress on Evolutionary Computation (CEC), Vol. II, pp. 1874–1881, Portland, USA (2004). IEEE Service center.
J.D. Knowles, D.W. Corne and M.J. Oates, On the assessment of multiobjective approaches to the adaptive distributed database management problem. In Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN VI) (2000) 869–878
J. Puchinger and G.R. Raidl, Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification, in First international Work-Conference on the Interplay between Natural and Artificial Computation (IWINAC) 3562 (2005) 41–53.
P. Smyth and R.M. Goodman, Knowledge Discovery in Databases, Chapter Rule Induction Using Information Theory, G. Piatetsky-Shapiro and J. Frawley (1991) 159–176.
Talbi, E.-G., A taxonomy of hybrid metaheuristics. Journal of Heuristics 8 (2002) 541–564. CrossRef
P-N. Tan, V. Kumar and J. Srivastava, Selecting the right interestingness measure for association patterns, in Proceedings of the Eight ACM SIGKDD conference, Edmonton, Canada (2002).
D.A.Van Veldhuizen and G.B. Lamont, On measuring multiobjective evolutionary algorithm performance, in In 2000 Congress on Evolutionary Computation. Piscataway, New Jersey, Vol. 1, 204–211 (2000).
K. Wang, S.H.W. Tay and B. Liu, Interestingness-based interval merger for numeric association rules, in edited by Proc. 4th Int. Conf. Knowledge Discovery and Data Mining, KDD, R. Agrawal, P. E. Stolorz, and G. Piatetsky-Shapiro, pp. 121–128. AAAI Press, (1998) 27–31. New York, USA.
Zaki, M.J., Parallel sequence mining on shared-memory machines. J. Parallel and Distrib. Comput. 61 (2001) 401–426. CrossRef
Zitzler, E. and Thiele, L., Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3 (1999) 257–271. CrossRef