Multi-objective rule mining using simulated annealing algorithm

Journal of Convergence Information Technology

Volumn 5, Issue 1, 2010, Pages 60-68

  Nasiri, Mehdi ^a   Taghavi, Leyla Sadat ^a   Minaee, Behrouz ^a  

^a IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Multi objective association rule mining; Numeric association rule mining; Simulated annealing

Indexed keywords

ASSOCIATION RULE MINING; COMPLETE CLASSIFICATION; INTERESTING RULES; INTERESTINGNESS; INTERESTINGNESS MEASURES; LARGE DATASETS; MULTI OBJECTIVE; MULTI-OBJECTIVE PROBLEM; RULE MINING; SIMULATED ANNEALING ALGORITHMS; SINGLE OBJECTIVE; SUPPORT COUNT;

ANNEALING; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; DATA MINING; MINING; MULTIOBJECTIVE OPTIMIZATION;

SIMULATED ANNEALING;

EID: 77956618767     PISSN: 19759320     EISSN: None     Source Type: Journal    
DOI: 10.4156/jcit.vol5.issue1.8     Document Type: Article

References (30)

1. Yan, X. et al."Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support", Expert Systems with Applications (2008), doi:10.1016/j.eswa.2008.01.028.
2. Chun-Hao Chen, Tzung-Pei Hong, and Vincent S. Tseng."A Cluster-Based Fuzzy-Genetic Mining Approach for Association Rules and Membership Functions". IEEE 2006.
3. R. Agrawal, T. Imielinski, A. Swami, "Mining association rules between sets of items in large databases". in: Proceedings of ACM SIGMOD Conference on Management of Data, 1993,pp. 207-216.
4. M.J. Berry, G. Linoff, "Data Mining Techniques for Marketing, Sales and Customer Support", JohnWiley and Sons, New York, 1997.
5. M. Kayaa, R. Alhajj. "Genetic algorithm based framework for mining fuzzy association rules".2005.
6. Hung-Pin Chiu, Yi-Tsung Tang, and Kun-Lin Hsieh,"A Cluster-Based Method for Mining Generalized Fuzzy Association Rules". IEEE2006.
7. Dehuri, Ghosh. "Multi-Objective Association Rule Mining Using Genetic Algorithm" Elsewier. 2004.
8. Dehuri, Ghosh. "Multi-Objective Genetic Algorithm For Assiciation Rule Mining Using A Homogeneos Dedicated Cluster of Work Station". Journal.2006.
9. Nasiri Mahdi, Minaei Behrouz, "Association Rule Mining Based Clustering Using Genetic Algotirhm".WIT Press.2008.
10. Gilal Alatas, Erhan Akin. "Chaos Rough particle swarm optimization and its applications". elsewier. 2008.
11. M.J. Berry, G. Linoff, "Data Mining Techniques for Marketing, Sales and Customer Support", John Wiley and Sons, New York, 1997.
12. K.J. Cios, W. Pedrycz, R.W. Swiniarski, "Data Miming Methods for Knowledge Discovery", Kluwer Academic Publishers, Boston, MA, 2000.
13. M.S. Chen, J. Han, P.S. Yu, "Data mining an overview from a database Transactions on Knowledge and Data Engineering", 6 (1996) 866-883.
14. W. Frawley, G. Piatasky-Saprio, C. Matheus, "Knowledge Discovery in Data Bases: An Overview", AAAI/MIT Press, 1991.
15. J. Han, M. Kamber, "Data Mining--Concepts and Techniques", Morgan Kaufmann, 2001.
16. M.V. Fedelis et al., "Discovering comprehensible classification rules with a genetic algorithm", in: Proceedings of Congress on Evolutionary Computation, 2000.
17. A.A. Freitas, "Data Mining and Knowledge Discovery with Evolutionary Algorithms", Springer-Verlag, New York, 2002.
18. E. Noda et. al., "Discovering interesting prediction rules with a genetic algorithm", Proceedings of the Congress on Evolutionary Computation 1999, pp. 1322-1329.
19. R. Agrawal, R. Srikant, "Fast algorithms for mining association rules", in: Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 1994.
20. A.M. Ayad, "A new algorithm for incremental mining of constrained association rules". Master Thesis, Department of Computer Sciences and Automatic Control, Alexandria University, 2000.
21. J. Hipp et al., "Algorithms for association rule mining-- a general survey and comparison", SIGKDD Explorations 2 (1) (2000) 1.
22. A.A. Freitas, "A survey of evolutionary algorithms for data mining and knowledge discovery", in: A. Ghosh, S. Tsutsui (Eds.), Advances in Evolutionary Computing, Springer-Verlag, New York, 2003, pp. 819-845.
23. H. Vafaie, K. DeJong, "Improving the performance of a rule induction system using genetic algorithms", in: R. Michalski, G. Tecuci (Eds.), Machine Learning--A Multistrategy Approach, vol. IV, Morgan Kaufmann Publishers, San Francisco, 1994, pp. 453-470.
24. S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi. 1983. "Optimization by Simulated Annealing". Science, vol 220, No. 4598, pp671-680.
25. R. A. Rutenber, Simulated Annealing Algorithms: An Overview, IEEE Circuits and Devices, Jan. 1989, pp19-26.
26. http://www.cs.nott.ac.uk/~gxk/aim/notes/simulatedannealing.doc
27. Mata J, Alvarez JL, Riquelme JC (2002), "Discovering numeric associationrules via evolutionary algorithm". In: Sixth Pacific-Asia conference on knowledge discovery and data mining PAKDD-02 (LNAI), Taiwan 2336, pp 40-51.
28. Alatas B, Akin E (2006), "An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules". Soft Comput 10(3):230-237
29. Moscato, P. (1993) An introduction to population approaches for optimization and hierarchical objective functions: a discussion on the role of tabu search. Annals of Operations Research, 41, 85-121.
30. Lingras P, Davies C (2001) Application of rough genetic algorithms.Comput Intell 17(2):435-445