Convergence and Consistency of Fuzzy c-means/ISODATA Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
A coevolutionary genetic algorithm using fuzzy clustering
Intelligent Data Analysis
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]
IEEE Transactions on Fuzzy Systems
An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering
IEEE Transactions on Fuzzy Systems
Will the real iris data please stand up?
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Fuzzy genetic sharing for dynamic optimization
International Journal of Automation and Computing
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This paper presents a new approach to unsupervised pattern classification. The classification scheme consists of two main stages. The first one is an unsupervised fuzzy learning procedure, which allows, using a similarity measure and a corresponding threshold, to seek clusters within a set of totally unlabeled samples. It provides, for each detected cluster, a good initial prototype as well as the membership degree of each sample. The second stage is an optimization procedure involving the fuzzy c-means (FCM) algorithm. Both procedures are repeated for different values of the similarity threshold, and three validity criteria are used to assess and rank the quality of all resulting partitions. The effectiveness of this approach is demonstrated, for different parameter values, on both artificial and real test data.