The formation and use of abstract concepts in design
Concept formation knowledge and experience in unsupervised learning
Data mining with neural networks: solving business problems from application development to decision support
Determining number of clusters and prototype locations via multi-scale clustering
Pattern Recognition Letters
On finding the number of clusters
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Applying a Clustering Genetic Algorithm for Extracting Rules from a Supervised Neural Network
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A new multiobjective clustering technique based on the concepts of stability and symmetry
Knowledge and Information Systems
Missing values imputation for a clustering genetic algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Evolving clusters in gene-expression data
Information Sciences: an International Journal
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
A Bayesian imputation method for a clustering genetic algorithm
Journal of Computational Methods in Sciences and Engineering
Multi-Objective Genetic Algorithm for Robust Clustering with Unknown Number of Clusters
International Journal of Applied Evolutionary Computation
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
International Journal of Hybrid Intelligent Systems
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This paper describes a new approach to find the right clustering of a dataset. We have developed a genetic algorithm to perform this task. A simple encoding scheme that yields to constant-length chromosomes is used. The objective function maximizes both the homogeneity within each cluster and the heterogeneity among clusters. Besides, the clustering genetic algorithm also finds the right number of clusters according to the Average Silhouette Width criterion. We have also developed specific genetic operators that are context-sensitive. Four examples are presented to illustrate the efficacy of the proposed method.