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
Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Fuzzy clustering using scatter matrices
Computational Statistics & Data Analysis - Special issue on classification
Analysis of the weighting exponent in the FCM
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Clustering algorithms based on volume criteria
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
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
Fuzzy algorithms for combined quantization and dithering
IEEE Transactions on Image Processing
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
The structural clustering and analysis of metric based on granular space
Pattern Recognition
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
An alternative fuzzy compactness and separation clustering algorithm
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Fuzzy cluster centers separation clustering using possibilistic approach
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
A novel fuzzy clustering algorithm with between-cluster information for categorical data
Fuzzy Sets and Systems
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Most clustering algorithms are based on a within-cluster scatter matrix with a compactness measure. In this paper we propose a novel fuzzy clustering algorithm, called the fuzzy compactness and separation (FCS), based on a fuzzy scatter matrix in which the FCS algorithm is derived using compactness measure minimization and separation measure maximization. The compactness is measured using a fuzzy within-cluster variation. The separation is measured using a fuzzy between-cluster variation. The proposed FCS objective function is a modification of the FS validity index proposed by Fukuyama and Sugeno and also a generalization of the fuzzy c-means (FCM). The FCS algorithm assigns a crisp boundary (cluster kernel) for each cluster such that hard memberships and fuzzy memberships can co-exist in the clustering results. Thus, FCS can be seen as a clustering algorithm with a novel sense between the hard c-means and fuzzy c-means. The FCS optimality tests and parameter selection are also investigated. Some numerical examples are demonstrated to show its robust properties and effectiveness.