Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A robust deterministic annealing algorithm for data clustering
Data & Knowledge Engineering
On fuzzy cluster validity indices
Fuzzy Sets and Systems
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A cluster validity index for fuzzy clustering
Information Sciences: an International Journal
Robust neural-fuzzy method for function approximation
Expert Systems with Applications: An International Journal
Clustering: A neural network approach
Neural Networks
Detecting and measuring rings in banknote images
Engineering Applications of Artificial Intelligence
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
An information-theoretic fuzzy C-spherical shells clustering algorithm
Fuzzy Sets and Systems
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Mathematical and Computer Modelling: An International Journal
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Relative entropy fuzzy c-means clustering
Information Sciences: an International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
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The fuzzy c spherical shells (FCSS) algorithm is specially designed to search for clusters that can be described by circular arcs or, generally, by shells of hyperspheres. A new approach to the FCSS algorithm is presented. This algorithm is computationally and implementationally simpler than other clustering algorithms that have been suggested for this purpose. An unsupervised algorithm which automatically finds the optimum number of clusters is not known. It uses a cluster validity measure to identify good clusters, merges all compatible clusters, and eliminates spurious clusters to achieve the final results. Experimental results on several data sets are presented