ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Stability-based validation of clustering solutions
Neural Computation
A flocking based algorithm for document clustering analysis
Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Nature-inspired applications and systems
Swarm Intelligence in Data Mining
Swarm Intelligence in Data Mining
Clustering by analytic functions
Information Sciences: an International Journal
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Fast global k-means clustering based on local geometrical information
Information Sciences: an International Journal
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Extracting different clusters of a given data is an appealing topic in swarm intelligence applications. This paper introduces two main data clustering approaches based on particle swarm optimization, namely single swarm and multiple cooperative swarms clustering. A stability analysis is next introduced to determine the model order of the underlying data using multiple cooperative swarms clustering. The proposed approach is assessed using different data sets and its performance is compared with that of k-means, k-harmonic means, fuzzy c-means and single swarm clustering techniques. The obtained results indicate that the proposed approach fairly outperforms the other clustering approaches in terms of different cluster validity measures.