Open AccessData Analysis by Combining the Modified K-Means and Imperialist Competitive Algorithm
Author(s) -
Mohammad Babrdelbonb,
Siti Zaiton Mohd Hashim,
Nor Erne Nazira Bazin
Publication year - 2014
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v70.3515
Subject(s) - imperialist competitive algorithm , cluster analysis , computer science , algorithm , process (computing) , field (mathematics) , evolutionary algorithm , evolutionary computation , computation , data mining , mathematical optimization , artificial intelligence , optimization problem , mathematics , meta optimization , pure mathematics , operating system
Data Clustering is one of the most used methods of data mining. The k-means Clustering Approach is one of the main algorithms in the literature of Pattern Recognition and Data Machine Learning which it very popular because of its simple application and high operational speed. But some obstacles such as the adherence of results to initial cluster centers or the risk of getting trapped into local optimality hinders its performance. In this paper, inspired by the Imperialist Competitive Algorithm based on the k-means method, a new approach is developed, in which cluster centers are selected and computed appropriately. The Imperialist Competitive Algorithm (ICA) is a method in the field of evolutionary computations, trying to find the optimum solution for diverse optimization problems. The underlying traits of this algorithm are taken from the evolutionary process of social, economic and political development of countries so that by partly mathematical modeling of this process some operators are obtained in regular algorithmic forms. The investigated results of the suggested approach over using standard data sets and comparing it with alternative methods in the literature reveals out that the proposed algorithm outperforms the k-means algorithm and other candidate algorithms in the pool.