Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Function approximation with polynomial membership functions and alternating cluster estimation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
Comments on “A possibilistic approach to clustering”
IEEE Transactions on Fuzzy Systems
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Mathematical and Computer Modelling: An International Journal
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Improving probabilities in a fuzzy clustering partition
Fuzzy Sets and Systems
A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction
Pattern Recognition Letters
Robust cluster validity indexes
Pattern Recognition
IEEE Transactions on Fuzzy Systems
Possibilistic shell clustering of template-based shapes
IEEE Transactions on Fuzzy Systems
Comparison of fuzzy clustering methods and their applications to geophysics data
Applied Computational Intelligence and Soft Computing
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
A new possibilistic clustering method: the possibilistic K-modes
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Fuzzy and possibilistic clustering for fuzzy data
Computational Statistics & Data Analysis
Fuzzy cluster centers separation clustering using possibilistic approach
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Visual data mining for identification of patterns and outliers in weather stations' data
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Improvements to the quantum evolutionary clustering
International Journal of Data Analysis Techniques and Strategies
Clustering using principal component analysis applied to autonomy-disability of elderly people
Decision Support Systems
Robust constrained fuzzy clustering
Information Sciences: an International Journal
On possibilistic clustering with repulsion constraints for imprecise data
Information Sciences: an International Journal
On the convergence of some possibilistic clustering algorithms
Fuzzy Optimization and Decision Making
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In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. Since the FCM memberships do not always explain the degrees of belonging for the data well, Krishnapuram and Keller proposed a possibilistic approach to clustering to correct this weakness of FCM. However, the performance of Krishnapuram and Keller's approach depends heavily on the parameters. In this paper, we propose another possibilistic clustering algorithm (PCA) which is based on the FCM objective function, the partition coefficient (PC) and partition entropy (PE) validity indexes. The resulting membership becomes the exponential function, so that it is robust to noise and outliers. The parameters in PCA can be easily handled. Also, the PCA objective function can be considered as a potential function, or a mountain function, so that the prototypes of PCA can be correspondent to the peaks of the estimated function. To validate the clustering results obtained through a PCA, we generalized the validity indexes of FCM. This generalization makes each validity index workable in both fuzzy and possibilistic clustering models. By combining these generalized validity indexes, an unsupervised possibilistic clustering is proposed. Some numerical examples and real data implementation on the basis of the proposed PCA and generalized validity indexes show their effectiveness and accuracy.