Volume 14, Issue 2 (9-2017)                   JSDP 2017, 14(2): 159-169 | Back to browse issues page

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Chaghari A, Feizi-Derakhshi M. Automatic Clustering Using Improved Imperialist Competitive Algorithm. JSDP. 2017; 14 (2) :159-169
URL: http://jsdp.rcisp.ac.ir/article-1-453-en.html
PhD Candidate University of Tabriz
Abstract:   (495 Views)

Imperialist Competitive Algorithm (ICA) is considered as a prime meta-heuristic algorithm to find the general optimal solution in optimization problems. This paper presents a use of ICA for automatic clustering of huge unlabeled data sets. By using proper structure for each of the chromosomes and the ICA, at run time, the suggested method (ACICA) finds the optimum number of clusters while optimal clustering of the data simultaneously.To increase the accuracy and speed of convergence, the structure of ICA changes. As in different applications, there is a need for data clustering which the number of clusters is not known before it is necessary to have methods that can cluster data without knowing the correct prediction of the number of clusters. In the other words, the proposed algorithm requires no background knowledge to classify the data.  In addition, the proposed method is more accurate in comparison with other clustering methods based on evolutionary algorithms. In Imperialist Competitive Algorithm, firstly steps should be taken to increase search rates and explore possible solution while approaching to the global optimal response the steps should be reduced to ensure that the algorithm is not lost and it is not in the local optimal manner. For this purpose and improvement of imperialist competitive algorithm, mutation rate and revolution operator's operation rate are determined dynamically. DB and CS are cluster validity Indexes. In this paper, DB and CS cluster validity measurements are used as the objective function. To demonstrate the superiority of the proposed method, the average of fitness function and the number of clusters determined by the proposed method is compared with three automatic clustering algorithms based on evolutionary algorithms. The partitional clustering algorithms are based on three powerful well-known optimization algorithms, namely the genetic algorithm, the particle swarm optimization and differential evolutionary algorithm.

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Type of Study: Research | Subject: Paper
Received: 2015/11/8 | Accepted: 2017/03/5 | Published: 2017/10/21 | ePublished: 2017/10/21

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