ACM Computing Surveys (CSUR)
Vector quantization based on genetic simulated annealing
Signal Processing
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Accurate integration of multi-view range images using k-means clustering
Pattern Recognition
A heuristic-based fuzzy co-clustering algorithm for categorization of high-dimensional data
Fuzzy Sets and Systems
Towards effective document clustering: A constrained K-means based approach
Information Processing and Management: an International Journal
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Adaptation of the F-measure to cluster based lexicon quality evaluation
Evalinitiatives '03 Proceedings of the EACL 2003 Workshop on Evaluation Initiatives in Natural Language Processing: are evaluation methods, metrics and resources reusable?
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A novel prototype generation technique for handwriting digit recognition
Pattern Recognition
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Clustering is one of the most commonly techniques in Data Mining. Kmeans is one of the most popular clustering techniques due to its simplicity and efficiency. However, it is sensitive to initialization and easily trapped in local optima. K-harmonic means clustering solves the problem of initialization using a built-in boosting function, but it is suffering from running into local optima. Particle Swarm Optimization is a stochastic global optimization technique that is the proper solution to solve this problem. In this paper, PSOKHM not only helps KHM clustering escape from local optima but also overcomes the shortcoming of slow convergence speed of PSO. In this paper, a hybrid data clustering algorithm based on PSO and Genetic algorithm, GSOKHM, is proposed. We investigate local optima method in addition to the global optima in PSO, called LSOKHM. The experimental results on five real datasets indicate that LSOKHM is superior to the GSOKHM algorithm.