Fuzzy-Possibilistic Classification: Resolution of Initialization Problem
Houria Boudouda*, Mohamed Nemissi*, Hamid Seridi*,**,
and Herman Akdag**,***
*Laboratoire d'Automatique et d'Informatique de Guelma (LAIG), Université 8 mai 1945 de Guelma, B.P.401, Guelma 24000, Algeria
**CResTIC, Université de Reims Champagne Ardenne, B.P.1035, 51687, Reims Cedex, France
***LIP6 Université P. & M. Curie, 75687 Paris Cedex 05, France
The methods of automatic classification resulting from the artificial intelligence are generally the consequences of a formalism based on an artificial reasoning quasi similar to that of the human expert. All the approaches of automatic classification developed so far, whether in an exact or approximate context, are dissociated from each other by the membership concept of an object to a class. In this paper, we present a new approach hybrid of unsupervised automatic classification under the C-Means (means of C classes) family. This new approach, based on the fusion of fuzzy and possibility theory and initialized by a membership matrix, allows on the one hand to solve simultaneously the problem of overlapping and coincidence, to reduce the noise effect and on the other hand to accelerate the classification process. The model validation is carried out by the FCM (Fuzzy C-Means), the PCM (Possibilistic C-Means)and the FPCM (Fuzzy-Possibilistic C-Means) for two cases of initialization by using Iris, Textured image and Tight human data basis.
and Herman Akdag, “Fuzzy-Possibilistic Classification: Resolution of Initialization Problem,” J. Adv. Comput. Intell. Intell. Inform., Vol.13, No.1, pp. 45-51, 2009.
-  J. C. Bezdek, “A Review of Probabilistic, Fuzzy, and Neural Models for Pattern Recognition,” Journal of Intelligent and Fuzzy Systems, Vol.1, pp. 1-25, 1993.
-  R. Krishnapuram and J. M. Keller, “A Possibilistic Approach to Clustering,” IEEE Trans Fuzzy Systems, Vol.1, pp. 98-110, May, 1993.
-  L. A. Zadeh, “Fuzzy Sets,” Inform. Control 8, pp. 338-353, 1965.
-  R. Krishnapuram and J. M. Keller, “The Possibilistic C-Means Algorithm: Insights and Recommendations,” IEEE Trans. Fuzzy Systems, Vol.4, pp. 385-396, August, 1996.
-  R. N. Dave, “characterization and detection of noise in clustring,” Pattern Recognition Lett., Vol.12, No.11, pp. 657-664, 1992.
-  R. N. Dave, “New measures for evaluating fuzzy partitions induced through C-shells clustering,” in proc. SPIE Conf. Intelligent Robots and Computer Vision X: Algorithms and Techniques (Boston), pp. 406-414, Nov. 1991.
-  M. Chavent, “A monotonic clustering method,” Pattern Recognition Letters 19, pp. 989-996, 1998.
-  L. Khodja, “Contribution à la classification floue non supervisé,” Thèse de l'Université de Savoie, 1997.
-  H. Boudouda and H. Seridi, “Une Nouvelle Approche de Classification Automatique Non Supervisée par C-Means : Fusion des Algorithmes Flou et Possibiliste,” proc. 2ème conférence internationale SETIT, Tunisie, p. 163, 2004.
-  H. Boudouda, M. Nemissi, H. Seridi, and H. Akdag, “Unsupervised Automatic Classification by C-Means: Resolution of Initialization Problem,” Al-Azhar Engineering Eighth Int. Conf. (AEIC 2004), Egypt, pp. 24-27, 2004.
-  Y. Cheng and M. Shift, “Mode Seeking and Clustering,” IEEE Trans. Pattern Analysis and Machine Vision, Vol.17(6), pp. 790-799, 1995.
-  F. Firenze and P. Morasso, “The Capture Effect Model: a New Approach to Self-Organized Clustering,” In Proc. of the Sixth Int. Conf. on Neural Networks and their Industrial and Cognitive Applications and Exhibition Catalog, NEURO-NIMES 93 Conf.. Nimes (France), pp. 45-54, 1993.
-  R. O. Duda, P. E. Hart, and D. G. Stork, “Pattern Classification,” Chap.10, pp. 542-548. Wiley- Interscience, 2 Edition 2001.
-  Rifqi and S. Monties, “Fuzzy prototypes for fuzzy data mining,” In O. Pons A.Vila et J. Kacprzyk, (edité par), Knowledge Management in Fuzzy Data bases, Physica-Verlag, 2000.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.