Academic
Publications
An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering,10.1016/j.engappai.2010.10.001,Engin

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering   (Citations: 7)
BibTex | RIS | RefWorks Download
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.
Journal: Engineering Applications of Artificial Intelligence - EAAI , vol. 24, no. 2, pp. 306-317, 2011
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: