A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
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IEEE Transactions on Knowledge and Data Engineering
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Metaheuristic Clustering
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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Information Sciences: an International Journal
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Mixed variable structural optimization using Firefly Algorithm
Computers and Structures
A scatter search algorithm for the automatic clustering problem
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
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In real life problems, prior information about the number of clusters is not known. In this paper, an attempt has been made to determine the number of clusters using automatic clustering using gravitational search algorithm (ACGSA). Based on the statistical property of datasets, two new concepts are proposed to efficiently find the optimal number of clusters. Within the ACGSA, a variable chromosome representation is used to encode the cluster centers with different number of clusters. In order to refine cluster centroids, two new operations namely threshold setting and weighted cluster centroid computation are also introduced. Finally, a new fitness function is proposed to make the search more efficient. A comparison of the proposed technique is also carried out with automatic clustering techniques developed recently. The proposed technique is further applied for automatic segmentation of both grayscale and color images and its performance is compared with other techniques. Experimental results demonstrate the efficiency and efficacy of the proposed clustering technique over other existing techniques.