A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering algorithms based on the maximum likelihood principle
Fuzzy Sets and Systems
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
Computational Statistics & Data Analysis - Special issue on classification
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
On finding the number of clusters
Pattern Recognition Letters
Fuzzy clustering with structural constraints
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New modified fuzzy C-means for determination of proper structure in dataset
Proceedings of the International Conference on Advances in Computing, Communication and Control
Fuzzy connectivity clustering with radial basis kernel functions
Fuzzy Sets and Systems
IEEE Transactions on Fuzzy Systems
The structural clustering and analysis of metric based on granular space
Pattern Recognition
Towards a standard methodology to evaluate internal cluster validity indices
Pattern Recognition Letters
New results on a fuzzy granular space
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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Classical fuzzy clustering methods are not able to compute a partition in a set of points when classes have nonconvex shape. Furthermore we know that in this case, the usual criteria of class validity such as fuzzy hypervolume or compactness-separability, do not allow to find the optimal partition. The purpose of our paper is to provide a clustering method able to divide a set of points into nonconvex classes without knowing a priori their number. We will show that it is possible to reconcile a fuzzy clustering method with a hierarchical ascending one while maintaining a fuzzy partition by a method called unsupervised fuzzy graph clustering. To that effect, we shall use the Fuzzy C-Means algorithm to divide the set of points into an overspecified number of subclasses. A fuzzy relation is then established between them in order to extract the structure of the set of points. It can be represented by a graduated hierarchy. Finally, we present a new criterion to find the cut of the hierarchy giving the optimal regrouping. This one allows to find the real classes existing into the set of points. The given results are compared with those obtained by other classical cluster validity criteria and we propose to study the influence of the number of initial subclasses on the final computed partition.