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Unsupervised Optimal Fuzzy Clustering
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A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A Clustering Performance Measure Based on Fuzzy Set Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of fuzzy clustering algorithms for pattern recognition. I
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
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Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Fuzzy Systems
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
Optimality test for generalized FCM and its application to parameter selection
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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On fuzzy cluster validity indices
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Pattern Recognition Letters
A cluster validity index for fuzzy clustering
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Extended fuzzy C-means clustering algorithm for hotspot events in spatial analysis
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A Family of Cluster Validity Indexes Based on a l-Order Fuzzy OR Operator
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Extended fuzzy C-means clustering in GIS environment for hot spot events
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Expert Systems with Applications: An International Journal
A cluster validity index for fuzzy clustering
Fuzzy Sets and Systems
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Information Technology and Management
A validity criterion for fuzzy clustering
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Self-organizing map for symbolic data
Fuzzy Sets and Systems
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Pattern Recognition
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Generalized agglomerative fuzzy clustering
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Cluster validity indexes have been used to evaluate the fitness of partitions produced by clustering algorithms. This paper presents a new validity index for fuzzy clustering called a partition coefficient and exponential separation (PCAES) index. It uses the factors from a normalized partition coefficient and an exponential separation measure for each cluster and then pools these two factors to create the PCAES validity index. Considerations involving the compactness and separation measures for each cluster provide different cluster validity merits. In this paper, we also discuss the problem that the validity indexes face in a noisy environment. The efficiency of the proposed PCAES index is compared with several popular validity indexes. More information about these indexes is acquired in series of numerical comparisons and also three real data sets of Iris, Glass and Vowel. The results of comparative study show that the proposed PCAES index has high ability in producing a good cluster number estimate and in addition, it provides a new point of view for cluster validity in a noisy environment.