Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
Simulated annealing: theory and applications
Simulated annealing: theory and applications
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks for pattern recognition
Neural networks for pattern recognition
A clustering algorithm based on graph connectivity
Information Processing Letters
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
An improved algorithm for clustering gene expression data
Bioinformatics
The evidence framework applied to classification networks
Neural Computation
Analysis of Biological Data: A Soft Computing Approach - Vol. 3
Analysis of Biological Data: A Soft Computing Approach - Vol. 3
A New Convergence Proof of Fuzzy c-Means
IEEE Transactions on Fuzzy Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative comparison of the performance of SAR segmentation algorithms
IEEE Transactions on Image Processing
Review: A particle swarm optimization approach to clustering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Computers in Biology and Medicine
Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects
Information Sciences: an International Journal
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Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.