Validating fuzzy partitions obtained through c-shells clustering
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
A genetic integrated fuzzy classifier
Pattern Recognition Letters - Special issue: Advances in pattern recognition
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
An integrated fuzzy cells-classifier
Image and Vision Computing
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
Robust cluster validity indexes
Pattern Recognition
A new Kernelized hybrid c-mean clustering model with optimized parameters
Applied Soft Computing
The structural clustering and analysis of metric based on granular space
Pattern Recognition
Expert Systems with Applications: An International Journal
Evolutionary fuzzy clustering of relational data
Theoretical Computer Science
Artificial Vision and Soft Computing
Fundamenta Informaticae
Fuzzy and hard clustering analysis for thyroid disease
Computer Methods and Programs in Biomedicine
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Clustering is primarily used to uncover the true underlying structure of a given data set and, for this purpose, it is desirable to subject the same data to several different clustering algorithms. This paper attempts to put an order on the various partitions of a data set obtained from different clustering algorithms. The goodness of each partition is expressed by means of a performance measure based on a fuzzy set decomposition of the data set under consideration. Several experiments reported in here show that the proposed performance measure puts an order on different partitions of the same data which is consistent with the error rate of a classifier designed on the basis of the obtained cluster labelings.