Algorithms for clustering data
Algorithms for clustering data
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Cluster validity methods: part I
ACM SIGMOD Record
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
Clustering validity checking methods: part II
ACM SIGMOD Record
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
On fuzzy cluster validity indices
Fuzzy Sets and Systems
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Rough-DBSCAN: A fast hybrid density based clustering method for large data sets
Pattern Recognition Letters
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
League Championship Algorithm: A New Algorithm for Numerical Function Optimization
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A New Solution Approach for Grouping Problems Based on Evolution Strategies
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A cluster validity index for fuzzy clustering
Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A size-insensitive integrity-based fuzzy c-means method for data clustering
Pattern Recognition
Hi-index | 0.01 |
Fuzzy data clustering plays an important role in practical use and has become a prerequisite step for decision-making in fuzzy environment. In this paper we propose a new algorithm, called FuzzyGES for unsupervised fuzzy clustering based on adaptation of the recently proposed Grouping Evolution Strategy (GES). To adapt GES for fuzzy clustering we devise a fuzzy counterpart of the grouping mutation operator typically used in GES, and employ it in a two phase procedure to generate a new clustering solution. Unlike conventional clustering algorithms which should get the number of clusters as an input, FuzzyGES tries to determine the true number of clusters as well as providing the optimal cluster centroids after several iterations. The proposed approach is validated through using several data sets and results are compared with those of fuzzy c-means algorithm, particle swarm optimization algorithm (PSO), differential evolution (DE) and league championship algorithm (LCA). We also investigate the performance of FuzzyGES through using different cluster validity indices. Our results indicate that FuzzyGES is fast and provides acceptable results in terms of both determining the correct number of clusters and the accurate cluster centroids.