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
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern classification with genetic algorithms
Pattern Recognition Letters - Special issue on genetic algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Simulated Annealing Using a Reversible Jump Markov Chain Monte Carlo Algorithm for Fuzzy Clustering
IEEE Transactions on Knowledge and Data Engineering
A novel population initialization method for accelerating evolutionary algorithms
Computers & Mathematics with Applications
Integrating clustering and supervised learning for categorical data analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new multi-objective technique for differential fuzzy clustering
Applied Soft Computing
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Memetic Algorithm for Multiple-Drug Cancer Chemotherapy Schedule Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this article, a new stochastic approach in form of memetic algorithm for fuzzy clustering is presented. The proposed probabilistic memetic algorithm based fuzzy clustering technique uses real-coded encoding of the cluster centres and two fuzzy clustering validity measures to compute a priori probability for an objective function. Moreover, the adaptive arithmetic recombination and opposite based local search techniques are used to get better performance of the proposed algorithm by exploring the search space more powerfully. The performance of the proposed clustering algorithm has been compared with that of some well-known existing clustering algorithms for four synthetic and two real life data sets. Statistical significance test based on analysis of variance (ANOVA) has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Matlab version of the software is available at http://sysbio.icm.edu.pl/memetic.