Greater knowledge extraction based on fuzzy logic and GKPFCM clustering algorithm

This work proposes how to generate a set of fuzzy rules from a data set using a clustering algorithm, the GKPFCM. If we recommend a number of clusters, the GKPFCM identifies the location and the approximate shape of each cluster. These ones describe the relations among the variables of the data set, and they can be expressed as conditional rules such as "If/Then". The GKPFCM provides membership and typicality values from which a knowledge base is generated through fuzzy rules, which can be used for the classification and characterization of new data.

[1]  Donald Gustafson,et al.  Fuzzy clustering with a fuzzy covariance matrix , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[2]  James M. Keller,et al.  A possibilistic fuzzy c-means clustering algorithm , 2005, IEEE Transactions on Fuzzy Systems.

[3]  James C. Bezdek,et al.  A mixed c-means clustering model , 1997, Proceedings of 6th International Fuzzy Systems Conference.

[4]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[5]  Marie-Jeanne Lesot.  Similarity , typicality and fuzzy prototypes for numerical data , 2005 .

[6]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[7]  James M. Keller,et al.  The possibilistic C-means algorithm: insights and recommendations , 1996, IEEE Trans. Fuzzy Syst..

[8]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[9]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[10]  Uzay Kaymak,et al.  Improved covariance estimation for Gustafson-Kessel clustering , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[11]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[12]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  Bernadette Bouchon-Meunier,et al.  Fuzzy Prototypes Based on Typicality Degrees , 2004, Fuzzy Days.

[14]  Dimitar Filev,et al.  Generation of Fuzzy Rules by Mountain Clustering , 1994, J. Intell. Fuzzy Syst..

[15]  D. Andina,et al.  An Improvement to the Possibilistic Fuzzy c-Means Clustering Algorithm , 2006, 2006 World Automation Congress.