Fuzzy sets in machine learning and data mining

Applied Soft Computing Journal

Volumn 11, Issue 2, 2011, Pages 1493-1505

  Hüllermeier, Eyke ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

Association rules; Clustering; Data mining; Decision trees; Feature extraction; Fuzzy sets; Gradual dependency; Machine learning

Indexed keywords

CLUSTERING; EMPIRICAL DATA; FUZZY METHODS; GRADUAL DEPENDENCIES; MACHINE-LEARNING; RESEARCH AREAS; TYPICAL APPLICATION;

ARTIFICIAL INTELLIGENCE; ASSOCIATION RULES; DATA MINING; DECISION TREES; FEATURE EXTRACTION; FUZZY LOGIC; FUZZY SETS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS;

FUZZY SET THEORY;

EID: 78751629935     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2008.01.004     Document Type: Conference Paper

References (58)

1. R. Agrawal, and R. Srikant Fast algorithms for mining association rules Proceedings of the 20th Conference on VLDB Santiago, Chile 1994 487 499
2. A. Al-Ani, and M. Deriche A new technique for combining multiple classifiers using the Dempster-Shafer theory of evidence J. Artif. Intell. Res. 17 2001 333 361
3. H. Bandemer, and W. Näther Fuzzy Data Analysis 1992 Kluwer Academic Publishers Dordrecht
4. A. Bargiela, and W. Pedrycz Granular Computing: An Introduction 2005 Kluwer Academic Publishers Boston, Dordrecht, London
5. S. Benferhat, D. Dubois, L. Garcia, and H. Prade On the transformation between possibilistic logic bases and possibilistic causal networks Int. J. Approx. Reason. 29 21 2002 35 173
6. S. Benferhat, and S. Smaoui Hybrid possibilistic networks Int. J. Approx. Reason. 44 3 2007 224 243
7. F. Berzal, J.C. Cubero, D. Sanchez, J.M. Serrano, and M.A. Vila An alternative approach to discover gradual dependencies Int. J. Uncertain. Fuzziness Knowl.-based Syst. 15 5 2007 559 570
8. C. Borgelt, and R. Kruse Graphical Models - Methods for Data Analysis and Mining 2002 Wiley Chichester
9. J. Casillas, O. Cordon, F. Herrera, L. Magdalena, Interpretability Issues in Fuzzy Modeling. Studies in Fuzziness and Soft Computing 2003 Springer-Verlag Berlin
10. G. Chen, Q. Wei, E. Kerre, and G. Wets Overview of fuzzy associations mining Proceedings of the ISIS-2003, 4th International Symposium on Advanced Intelligent Systems Jeju, Korea, September 2003
11. S. Cho, and J. Kim Combining multiple neural network by fuzzy integral for robust classification IEEE Trans. Syst. Man Cybern. 25 1995 380 384
12. O. Cordon, M.J. del Jesus, and F. Herrera Analyzing the reasoning mechanisms in fuzzy rule based classification systems Mathware Soft Comput. 5 1998 321 332
13. O. Cordon, F. Gomide, F. Herrera, F. Hoffmann, and L. Magdalena Ten years of genetic fuzzy systems: current framework and new trends Fuzzy Sets Syst. 141 1 2004 5 31
14. T.H. Dang, B. Bouchon-Meunier, and C. Marsala Measures of information for inductive learning Proceedings of the IPMU-2004 Perugia, Italy 2004
15. M. Delgado, N. Marin, D. Sanchez, and M.A. Vila Fuzzy association rules: general model and applications IEEE Trans. Fuzzy Syst. 11 2 2003 214 225
16. P. Diamond, and P. Kloeden Metric Spaces of Fuzzy Sets: Theory and Applications 1994 World Scientific Singapore
17. P. Diamond, and H. Tanaka Fuzzy regression analysis R. Slowinski, Fuzzy Sets in Decision Analysis, Operations Research and Statistics 1998 Kluwer 349 387
18. D. Dubois, F. Dupin de Saint-Cyr, and H. Prade A possibility-theoretic view of formal concept analysis Fundamenta Informaticae 76 2006 1 19
19. D. Dubois, F. Esteva, P. Garcia, L. Godo, R. Lopez de Mantaras, and H. Prade Fuzzy set modelling in case-based reasoning Int. J. Intell. Syst. 13 1998 345 373
20. D. Dubois, E. Hüllermeier, H. Prade, A systematic approach to the assessment of fuzzy association rules, Data Min. Knowl. Discov. 13 (2) 167.
21. D. Dubois, E. Hüllermeier, and H. Prade Fuzzy set-based methods in instance-based reasoning IEEE Trans. Fuzzy Syst. 10 3 2002 322 332
22. D. Dubois, E. Hüllermeier, and H. Prade A note on quality measures for fuzzy association rules Proceedings of the IFSA-03, 10th International Fuzzy Systems Association World Congress, number 2715 in LNAI 2003 Springer-Verlag Istambul 648 677
23. D. Dubois, and H. Prade Gradual inference rules in approximate reasoning Inform. Sci. 61 1, 2 1992 103 122
24. D. Dubois, H. Prade, What are fuzzy rules and how to use them, 84 (1996) 169-185.
25. U.M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth From data mining to knowledge discovery: an overview Advances in Knowledge Discovery and Data Mining 1996 MIT Press
26. A.P. Gasch, and M.B. Eisen Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering Genome Biol. 3 11 2002 1 22
27. J. Gebhardt, and R. Kruse A possibilistic interpretation of fuzzy sets by the context model Proceedings of the IEEE International Conference on Fuzzy Systems 1992 1089 1096
28. F. Höppner, F. Klawonn, F. Kruse, and T. Runkler Fuzzy Cluster Analysis 1999 Wiley Chichester
29. E. Hüllermeier Implication-based fuzzy association rules Proceedings of the PKDD-01, 5th European Conference on Principles and Practice of Knowledge Discovery in Databases Freiburg, Germany 2001 241 252
30. E. Hüllermeier Association rules for expressing gradual dependencies Proceedings of the PKDD-02, 6th European Conference on Principles and Practice of Knowledge Discovery in Databases Helsinki, Finland 2002 200 211
31. E. Hüllermeier Possibilistic induction in decision tree learning Proceedings of the ECML-02, 13th European Conference on Machine Learning Helsinki, Finland 2002 173 184
32. E. Hüllermeier Possibilistic instance-based learning Artif. Intell. 148 1/2 2003 335 383
33. E. Hüllermeier Cho-k-NN: a method for combining interacting pieces of evidence in case-based learning Proceedings of the IJCAI-05, 19th International Joint Conference on Artificial Intelligence Edinburgh, Scotland 2005 3 8
34. E. Hüllermeier, I. Renners, and A. Grauel An evolutionary approach to constraint-regularized learning Mathware Soft Comput. 11 2/3 2004 109 124
35. E. Hüllermeier Fuzzy sets in machine learning and data mining: status and prospects Fuzzy Sets Syst. 156 3 2005 387 406
36. C.Z. Janikow Fuzzy decision trees: issues and methods IEEE Trans. Syst. Man Cybern. 28 1 1998 1 14
37. R. Krishnapuram, and J.M. Keller A possibilistic approach to clustering IEEE Trans. Fuzzy Syst. 1 2 1993 98 110
38. A. Laurent Generating fuzzy summaries: a new approach based on fuzzy multidimensional databases Intell. Data Anal. J. 7 2 2003 155 177
39. S.L. Lauritzen, and D.J. Spiegelhalter Local computations with probabilities on graphical structures and their application to expert systems J. Roy. Stat. Soc. Ser. B 50 2 1988 157 224
40. C.H.L. Lee, A. Liu, and W.S. Chen Pattern discovery of fuzzy time series for financial prediction IEEE Trans. Knowl. Data Eng. 18 5 2006 613 625
41. T.Y. Lin, Y.Y. Yao, L.A. Zadeh, Data Mining, Rough Sets and Granular Computing 2002 Physica-Verlag Heidelberg
42. T.M. Mitchell, The need for biases in learning generalizations, Technical Report TR CBM-TR-117, Rutgers University, 1980.
43. D. Nauck, F. Klawonn, and R. Kruse Foundations of Neuro-Fuzzy Systems 1997 Wiley and Sons Chichester, UK
44. C. Olaru, and L. Wehenkel A complete fuzzy decision tree technique Fuzzy Sets Syst. 138 2 2003
45. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufmann San Mateo, CA
46. W. Pedrycz Knowledge-based Clustering 2005 John Wiley & Sons
47. W. Pedrycz, and Z.A. Sosnowski Designing decision trees with the use of fuzzy granulation IEEE Trans. Syst. Man Cybern., Part A 30 2 2000 151 159
48. W. Pedrycz, and Z.A. Sosnowski The design of decision trees in the framework of granular data and their application to software quality models Fuzzy Sets Syst. 123 3 2001 271 290
49. W. Pedrycz, and Z.A. Sosnowski C-fuzzy decision trees IEEE Trans. Syst. Man Cybern., Part C 35 4 2005 498 511
50. H. Prade Raisonner avec des rgles d'inférence graduelle - Une approche basée sur les ensembles flous Revue d'Intelligence Artificielle 2 2 1988 29 44
51. E.H. Ruspini Possibility as similarity: the semantics of fuzzy logic P.P. Bonissone, H. Henrion, L.N. Kanal, J.F. Lemmer, Uncertainty in Artificial Intelligence, vol. 6 1991 Elsevier Science Publisher
52. A. Savasere, E. Omiecinski, and S. Navathe An efficient algorithm for mining association rules in large databases VLDB-95, Proceedings of the 21st International Conference on Very Large Data Bases Zurich, September 1995 11 15
53. T. Sudkamp Similarity as a foundation for possibility Proceedings of the 9th IEEE International Conference on Fuzzy Systems San Antonio 2000 735 740
54. T. Sudkamp Examples, counterexamples, and measuring fuzzy associations Fuzzy Sets Syst. 149 1 2005
55. R. Viertl Statistical Methods for Non-precise Data 1996 CRC Press Boca Raton, Florida
56. R. Weber Fuzzy-ID3: a class of methods for automatic knowledge acquisition IIZUKA-92, Proceedings of the 2nd International Conference on Fuzzy Logic, vol. 1 1992 265 268
57. R.R. Yager Soft aggregation methods in case based reasoning Appl. Intell. 21 2004 277 288
58. L.A. Zadeh New approach to the analysis of complex systems IEEE Trans. Syst. Man Cybern. 3 1 1973