A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A genetic algorithm for cluster analysis
Intelligent Data Analysis
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Introducing dynamic diversity into a discrete particle swarm optimization
Computers and Operations Research
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
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving clusters in gene-expression data
Information Sciences: an International Journal
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
LADPSO: using fuzzy logic to conduct PSO algorithm
Applied Intelligence
Engineering Applications of Artificial Intelligence
Ensemble canonical correlation analysis
Applied Intelligence
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
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Combinatorial Particle Swarm Optimization (CPSO) is a relatively recent technique for solving combinatorial optimization problems. CPSO has been used in different applications, e.g., partitional clustering and project scheduling problems, and it has shown a very good performance. In partitional clustering problem, CPSO needs to determine the number of clusters in advance. However, in many clustering problems, the correct number of clusters is unknown, and it is usually impossible to estimate. In this paper, an improved version, called CPSOII, is proposed as a dynamic clustering algorithm, which automatically finds the best number of clusters and simultaneously categorizes data objects. CPSOII uses a renumbering procedure as a preprocessing step and several extended PSO operators to increase population diversity and remove redundant particles. Using the renumbering procedure increases the diversity of population, speed of convergence and quality of solutions. For performance evaluation, we have examined CPSOII using both artificial and real data. Experimental results show that CPSOII is very effective, robust and can solve clustering problems successfully with both known and unknown number of clusters. Comparing the obtained results from CPSOII with CPSO and other clustering techniques such as KCPSO, CGA and K-means reveals that CPSOII yields promising results. For example, it improves 9.26 % of the value of DBI criterion for Hepato data set.