A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Unsupervised possibilistic clustering
Pattern Recognition
Upper and lower values for the level of fuzziness in FCM
Information Sciences: an International Journal
Fuzzy possibility C-Mean based on mahalanobis distance and separable criterion
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Missing Clusters Indicate Poor Estimates or Guesses of a Proper Fuzzy Exponent
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
ACS'08 Proceedings of the 8th conference on Applied computer scince
A stability based validity method for fuzzy clustering
Pattern Recognition
A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests
Pattern Recognition Letters
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A new FCM-based algorithm of hydrophobic image segmentation
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
Locality sensitive C-means clustering algorithms
Neurocomputing
An improved FCM clustering method for interval data
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Fuzzy clustering of time series in the frequency domain
Information Sciences: an International Journal
Shadowed sets in the characterization of rough-fuzzy clustering
Pattern Recognition
Analysis of parameter selections for fuzzy c-means
Pattern Recognition
No reference image quality assessment using fuzzy relational classifier
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Pattern Recognition
MiniMax ε-stable cluster validity index for Type-2 fuzziness
Information Sciences: an International Journal
A novel fuzzy c-means clustering algorithm
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
An improved clustering algorithm for information granulation
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Feature interaction in subspace clustering using the Choquet integral
Pattern Recognition
An evolutionary fuzzy clustering with minkowski distances
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Partitive clustering (K-means family)
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Objective function-based clustering
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
The range of the value for the fuzzifier of the fuzzy c-means algorithm
Pattern Recognition Letters
Generalized agglomerative fuzzy clustering
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Fuzzy clustering of human activity patterns
Fuzzy Sets and Systems
A novel fuzzy clustering algorithm with between-cluster information for categorical data
Fuzzy Sets and Systems
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives
International Journal of Approximate Reasoning
A multivariate fuzzy c-means method
Applied Soft Computing
Evolving soft subspace clustering
Applied Soft Computing
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The fuzzy c-means (FCM) algorithm is one of the most frequently used clustering algorithms. The weighting exponent m is a parameter that greatly influences the performance of the FCM. But there has been no theoretical basis for selecting the proper weighting exponent in the literature. In this paper, we develop a new theoretical approach to selecting the weighting exponent in the FCM. Based on this approach, we reveal the relation between the stability of the fixed points of the FCM and the data set itself. This relation provides the theoretical basis for selecting the weighting exponent in the FCM. The numerical experiments verify the effectiveness of our theoretical conclusion.