A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
An efficient k'-means clustering algorithm
Pattern Recognition Letters
GAKREM: A novel hybrid clustering algorithm
Information Sciences: an International Journal
A search space reduction methodology for data mining in large databases
Engineering Applications of Artificial Intelligence
Unsupervised cluster discovery using statistics in scale space
Engineering Applications of Artificial Intelligence
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic algorithms to simplify prognosis of endocarditis
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
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Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.