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Possibilistic Exponential Fuzzy Clustering
Journal of Computer Science and Technology, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Treerattanapitak, Kiatichai +1 more
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FUZZY CLUSTERING BASED ON INTUITIONISTIC FUZZY RELATIONS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004It is well known that an intuitionistic fuzzy relation is a generalization of a fuzzy relation. In fact there are situations where intuitionistic fuzzy relations are more appropriate. This paper discusses the fuzzy clustering based on intuitionistic fuzzy relations. On the basis of max -t & min -s compositions, we discuss an n-step procedure which
Hung, Wen-Liang +2 more
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Fuzzy clustering: Determining the number of clusters
2012 Fourth International Conference on Computational Aspects of Social Networks (CASoN), 2012In this study we analyze behavior of two types of coefficients for determining the suitable number of clusters obtained when fuzzy cluster analysis is applied. First one is Dunn's coefficient which contains membership degrees in its computational formula; second one is the average silhouette width, used primarily for evaluating hard clustering.
Řezanková, H., Húsek, D. (Dušan)
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Fuzzy clustering with outliers
PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2002In this paper we introduce a modified objective function for fuzzy clustering. We add an additional weighting factor for each datum and derive necessary conditions for the introduced parameter in order to optimise the objective function. These conditions are used in an alternating optimisation scheme to calculate a partition of sample data.
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IEEE International Conference on Neural Networks, 2002
An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
D.B. Fogel, P.K. Simpson
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An alternate training technique for the fuzzy min-max clustering neural network is introduced. The original fuzzy min-max clustering neural network utilized an algorithm similar to leader clustering and adaptive resonance theory to place hyperboxes in the pattern space.
D.B. Fogel, P.K. Simpson
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Pattern Recognition Letters, 1995
Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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Abstract The proposed clustering algorithm is aimed at revealing the structure within the patterns under a simultaneous satisfaction of directionality constraints. These constraints are utilized to cope with functional relationships between the specified features of the patterns.
Kaoru Hirota, Witold Pedrycz
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2018
The objective of this thesis is to represent an alternative clustering technique which is among the most famous, called as Fuzzy c-Means algorithm. Fuzzy clustering methods, like Fuzzy c-Means and Gustafson Kessel extension, allow the objects to belong to several clusters simultaneously with different degrees of membership.
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The objective of this thesis is to represent an alternative clustering technique which is among the most famous, called as Fuzzy c-Means algorithm. Fuzzy clustering methods, like Fuzzy c-Means and Gustafson Kessel extension, allow the objects to belong to several clusters simultaneously with different degrees of membership.
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Fuzziness indices for fuzzy clustering
The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03., 2004Some indices of fuzziness are introduced for providing helpful information in fuzzy clustering. These indices play an auxiliary role in fuzzy clustering and can be used for deciding the number of clusters by combining with another criterion. Numerical examples are given for demonstrating how these indices can be applied.
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[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 2002
The authors introduce a new hybrid method called fuzzy elastic clustering for clustering and classification of patterns. It generates closed loops around a set of training patterns and sections off portions of the hyperspace thus enclosing clusters of patterns.
R. Srikanth, F.E. Petry, C. Koutsougeras
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The authors introduce a new hybrid method called fuzzy elastic clustering for clustering and classification of patterns. It generates closed loops around a set of training patterns and sections off portions of the hyperspace thus enclosing clusters of patterns.
R. Srikanth, F.E. Petry, C. Koutsougeras
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Deep FC-IIWO Fuzzy Clustering: An Optimized Fuzzy Clustering Approach for Big Data Clustering
International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsA significant role is played by clustering approaches in data mining processes, which becomes more challenging because of the rising dimension of the available databases. Clustering strategies are employed in various sectors like information retrieval, social network analytics, image processing, and so on.
Sudha, D., Gowri, S.
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