Handbook of pattern recognition & computer vision
Handbook of pattern recognition & computer vision
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
A comparison of cluster validity criteria for a mixture of normal distributed data
Pattern Recognition Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering validity checking methods: part II
ACM SIGMOD Record
Fuzzy Sets and Systems - Clustering and modeling
A new approach for measuring the validity of the fuzzy c-means algorithm
Advances in Engineering Software
Cluster center initialization algorithm for K-means clustering
Pattern Recognition Letters
A cluster validation index for GK cluster analysis based on relative degree of sharing
Information Sciences—Informatics and Computer Science: An International Journal
A cluster validity index for fuzzy clustering
Pattern Recognition Letters
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
New indices for cluster validity assessment
Pattern Recognition Letters
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Unsupervised possibilistic clustering
Pattern Recognition
Fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data
Pattern Recognition
A method for initialising the K-means clustering algorithm using kd-trees
Pattern Recognition Letters
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Correction to "On Cluster Validity for the Fuzzy c-Means Model" [Correspondence]
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
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Fuzzy c-means (FCM) and its variants suffer from two problems--local minima and cluster validity--which have a direct impact on the formation of final clustering. There are two strategies--optimization and center initialization strategies--that address the problem of local minima. This paper proposes a center initialization approach based on a minimum spanning tree to keep FCM from local minima. With regard to cluster validity, various strategies have been proposed. On the basis of the fuzzy cluster validity index, this paper proposes a selection model that combines multiple pairs of a fuzzy clustering algorithm and cluster validity index to identify the number of clusters and simultaneously selects the optimal fuzzy clustering for a dataset. The promising performance of the proposed center-initialization method and selection model is demonstrated by experiments on real datasets.