Multi-criteria evaluation of interesting dependencies according to a data mining approach

2003 Congress on Evolutionary Computation, CEC 2003 - Proceedings

Volumn 3, Issue , 2003, Pages 1568-1574

  Francisci, Dominique ^a   Collard, Martine ^a  

^a UNIVERSITY OF NICE SOPHIA ANTIPOLIS   (France)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; EVOLUTIONARY ALGORITHMS;

COMPARATIVE STUDIES; DEPENDENCY RULES; INTERESTING RULES; MULTI-CRITERIA EVALUATION; MULTI-OBJECTIVE EVOLUTIONARY; MULTIPLE FACTORS; RELATIVE CONTRIBUTION; STANDARD SOLUTIONS;

DATA MINING;

EID: 33751362136     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2003.1299859     Document Type: Conference Paper

References (19)

1. R. Agrawal and A. Srikant. Fast algorithms for mining association rules. In Proc. VLDB 1994, pages 487- 499, 1994.
2. W. Hsu, B. Liu and Y. Ma. Integrating classification and association rule mining. In Proceedings of the 4th lnternatiorial Conference on Knowledge Discovery and Data Mining (KDD-1998), Plenav Presentation), New, York, 1998.
3. th International Conference on Knowledge Discovery and Data Minirig (KDD-2000), Boston, August 2000. AAAI Press.
4. H. Mannila, D. J. Hand and P. Smyth. Principles of data mining. Adaptive Computation and Machine Learning. J. Wolfskill, 5 Cambridge Center, 4th floor, Cambridge, MA 02142, 2001.
5. H.S. Lopes, D.L.A. Araujo and A.A. Freitas. A parallel genetic algorithm for rule discovery in large databases. In Proc. 1999 IEEE Sysrenis, Man arid Cybernetics Conf, v. III, pages 940-945, Tokyo, October 1999.
6. C. Emmanouilidis. Evolutionary multi-objective feature selection and rw analysis with application to industrial machinery fault diagnosis. In Eurogen 2001, 2001.
7. I. W. Flockan and N. J. Radcliffe. Ga-miner : Parallel data mining with hierarchical genetic algorithms, final report. In EPCCAlKMS GA-Miner - Report 1.0, University of Edimburgh, November 1995.
8. A.A. Freitas. A multi-criteria approach for the evaluation of rule interestingness data mining. In Proc. brternaiional Conference, pages 7-20, Rio de Janeiro, Brazil, September 1998. WIT Press.
9. A. A. Freitas, G. L. Pappa and C. A. A. Kaestner. A multiobjective genetic algorithm for attribute selection. In A. Lofti J. Garibaldi and R. John, editors. Proc. of the 4th Internation Confererice on Recent Advarrces in Soft Computirig (RASC-2002), pages 116-121. Nottingham Trent University, December 2002.
10. R. M. Goodman and P. Smyth. Rule induction using information theory. In G. Piatetsky-Shapiro and W. J. Frawley, editors, Knowledge Discovery in Databases (KDD-1991). MITPress, 1991.
11. N. J. Radcliffe and P. D. Surry. Co-operation through hierarchical competition in genetic data mining. In EPCC-TR94-09, 1994.
12. International Business Machines. IBM intelligent Miner: User's guide. IBM, 1996.
13. rd International Conference on Knowledge Discoven arid Data Mining (KDD-1997), pages 3742, Newport Beach, California, August 1997. AAAI Press.
14. G. Piatetsky-Shapiro. Discovery, analysis and presentation of strong rules. In G. Piatetsky-Shapiro and editors W. J. Frawley, editors, Knowledge Discovey in Databases. MIT Press, 1991.
15. J.D. Ullman, S. Brin, R. Motwani and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proc. ACM SIGMOD Biternational Conference on Management of Data (SlGMOD-1997), Tucson, Arizona, USA, May 1997.
16. M. Sebag and M. Schoenauer. Generation of rules with certainty and confidence factors from incomplete and incoherent learning hases. In Proc. of the European Knowledge Acquisition Workshop EKAW-1988. 1988.
17. N. Srinivas and K. Deb. Multiobjective optimization using non-dominated sorting in genetic algorithms. Technical report, Department of Technical Engineering, Indian Institute of Technology, Kanput, Indian, 1993.
18. Irvine University. Uci machine learning ftp.ics.uci.edu/pub/machine- learning-databases.
19. D. Chou, V. Dhar and F. Provost. Discovering interesting patterns for investment decision making with glower - A genetic learner overlaid with entropy reduction. Data Mining and Knowledge Discoven Journal, 4(4):251-280, 2000.