51
Views
11
CrossRef citations to date
0
Altmetric
Original Articles

Finding Pareto-front Membership Functions in Fuzzy Data Mining

Pages 343-354 | Received 15 Dec 2010, Accepted 01 Jun 2011, Published online: 23 Apr 2012

References

  • Alcala-Fdez , J. , Alcala , R. , Gacto , M. J. and Herrera , F. 2009 . Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms . Fuzzy Sets and Systems , 160 ( 7 ) : 905 – 921 .
  • R. Alcalá , Y. Nojima , F. Herrera and H. Ishibuchi , “Multiobjective genetic fuzzy rule selection of single granularity-based fuzzy classification rules and its interaction with the lateral tuning of membership functions,” Soft Computing , doi: 10.1007/s00500-010-0671-2 , 2010 .
  • M. Antonelli , P. Ducange , B. Lazzerini and F. Marcelloni , Learning knowledge bases of multi-objective evolutionary fuzzy systems by simultaneously optimizing accuracy, complexity and partition integrity , Soft Computing , doi: 10.1007/s00500-010-0671-2 , 2010 .
  • Agrawal , R. , Imielinksi , T. and Swami , A. 1993 . Database mining: a performance perspective . IEEE Transactions on Knowledge and Data Engineering , 5 ( 6 ) : 914 – 925 .
  • Alhajj , R. and Kaya , M. 2008 . Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining . Journal of Intelligent Information Systems , 31 ( 3 ) : 243 – 264 .
  • R. Agrawal and R. Srikant , “Fast algorithm for mining association rules,” The International Conference on Very Large Databases , pp. 487 – 499 , 1994 .
  • Botta , A. , Lazzerini , B. , Marcelloni , F. and Stefanescu , D. 2009 . Context adaptation of fuzzy systems through a multi-objective evolutionary approach based on a novel interpretability index . Soft Computing , 13 ( 3 ) : 437 – 449 .
  • C. C. Chan and W. H. Au , “Mining fuzzy association rules,” The Conference on Information and Knowledge Management , Las Vegas , pp. 209 – 215 , 1997 .
  • C. H. Chen , T. P. Hong , Vincent S. Tseng and L. C. Chen , “A multi-objective genetic-fuzzy mining algorithm,” The 2008 IEEE International Conference on Granular Computing , pp. 115 – 120 , 2008 .
  • Chen , C. H. , Hong , T. P. , Tseng , Vincent S. and Lee , C. S. 2009 . A genetic-fuzzy mining approach for items with multiple minimum supports . Soft Computing , 13 ( 5 ) : 521 – 533 .
  • Chen , C. H. , Tseng , Vincent S. and Hong , T. P. 2008 . Cluster-based evaluation in fuzzy-genetic data mining . IEEE Transactions on Fuzzy Systems , 16 ( 1 ) : 249 – 262 .
  • Cordón , O. , Herrera , F. and Villar , P. 2001 . Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base . IEEE Transactions on Fuzzy Systems , 9 ( 4 ) : 667 – 674 .
  • C. A. Coello , D. A. Van Veldhuizen and G. B. Lamont , Evolutionary Algorithms for Solving Multi-objective Problems , Kluwer Academic Publishers , 2002 .
  • Deb , K. 2001 . Multi-objective Optimization Using Evolutionary Algorithms , John Wiley & Sons .
  • K. Deb , S. Agrawal , A. Pratab and T. Meyarivan , “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation , 6 2 , pp. 681 – 695 .
  • C. M. Fonseca and P. J. Fleming , Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization The International Confidence on Genetic Algorithms , pp. 416 – 423 , 1993 .
  • Gacto , M. J. , Alcalá , R. and Herrera , F. 2009 . Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems . Soft Computing , 13 ( 3 ) : 419 – 436 .
  • Hong , T. P. , Chen , C. H. , Wu , Y. L. and Lee , Y. C. 2008 . Genetic-Fuzzy Data Mining with Divide-and-Conquer Strategy . IEEE Transactions on Evolutionary Computation , 12 ( 2 ) : 252 – 265 .
  • Hong , T. P. , Chen , C. H. , Wu , Y. L. and Lee , Y. C. 2006 . A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions . Soft Computing , 10 ( 11 ) : 1091 – 1101 .
  • Hong , T. P. , Kuo , C. S. and Chi , S. C. 1999 . Mining association rules from quantitative data . Intelligent Data Analysis , 3 ( 5 ) : 363 – 376 .
  • Hong , T. P. , Kuo , C. S. and Chi , S. C. 2001 . Trade-off between time complexity and number of rules for fuzzy mining from quantitative data . International Journal of Uncertainty, Fuzziness and Knowledge-based Systems , 9 ( 5 ) : 587 – 604 .
  • Herrera , F. , Lozano , M. and Verdegay , J. L. 1997 . Fuzzy connectives based crossover operators to model genetic algorithms population diversity . Fuzzy Sets and Systems , 92 ( 1 ) : 21 – 30 .
  • Kaya , M. and Alhajj , R. 2005 . Genetic algorithm based framework for mining fuzzy association rules . Fuzzy Sets and Systems , 152 ( 3 ) : 587 – 601 .
  • Kaya , M. and Alhajj , R. 2006 . Utilizing genetic algorithms to optimize membership functions for fuzzy weighted association rules mining . Applied Intelligence , 24 ( 1 ) : 7 – 15 .
  • Kaya , M. and Alhajj , R. 2006 . Effective mining of fuzzy multi-cross-level weighted association rules . Lecture Notes in Computer Science , 4203 : 399 – 408 .
  • Kaya , M. 2006 . Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules . Soft computing , 10 : 578 – 586 .
  • Kuok , C. , Fu , A. and Wong , M. 1998 . Mining fuzzy association rules in databases . SIGMOD Record , 27 ( 1 ) : 41 – 46 .
  • Lee , Y. C. , Hong , T. P. and Lin , W. Y. 2004 . Mining fuzzy association rules with multiple minimum supports using maximum constraints” . Lecture Notes in Computer Science , 3214 : 1283 – 1290 .
  • Roubos , H. and Setnes , M. 2001 . Compact and transparent fuzzy models and classifiers through iterative complexity reduction . IEEE Transactions on Fuzzy Systems , 9 ( 4 ) : 516 – 524 .
  • J. D. Schaffer , “Multiple objective optimization with vector evaluated genetic algorithms,” The International Conference on Genetic Algorithms , pp. 93 – 100 , 1985 .
  • R. Srikant and R. Agrawal , “Mining quantitative association rules in large relational tables,” The 1996 ACM SIGMOD International Conference on Management of Data , , Monreal , , Canada, June 1996 , pp. 1 – 12 .
  • Wang , C. H. , Hong , T. P. and Tseng , S. S. 2000 . Integrating membership functions and fuzzy rule sets from multiple knowledge sources . Fuzzy Sets and Systems , 112 : 141 – 154 .
  • S. Yue , E. Tsang , D. Yeung and D. Shi , “Mining fuzzy association rules with weighted items,” The IEEE International Conference on Systems, Man and Cybernetics , pp. 1906 – 1911 , 2000 .
  • Z. Zhang , Y. Lu and B. Zhang , “An effective partitioning-combining algorithm for discovering quantitative association rules,” The Pacific-Asia Conference on Knowledge Discovery and Data Mining , pp. 261 – 270 , 1997 .
  • E. Zitzler , M. Laumanns and L. Thiele , “SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization, ” Proc. Evolutionary Methods for Design, Optimization and Control with App. to Industrial Problems , Barcelona , , Spain , 2001 pp. 95 – 100 .

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.