International Journal of Advances in Intelligent Informatics
Vol 6, No 1 (2020): March 2020

Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

Alwatben Batoul Rashed (Department of Information Technology, Qassim University)
Hazlina Hamdan (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)
Nurfadhlina Mohd Sharef (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)
Md Nasir Sulaiman (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)
Razali Yaakob (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)
Mansir Abubakar (Faculty of Computer Science and Information Technology, Universiti Putra Malaysia)



Article Info

Publish Date
31 Mar 2020

Abstract

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various fields to divide and restructure data to become more significant and transform them into more useful information. Generally, clustering is difficult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.

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Journal Info

Abbrev

IJAIN

Publisher

Subject

Computer Science & IT

Description

International journal of advances in intelligent informatics (IJAIN) e-ISSN: 2442-6571 is a peer reviewed open-access journal published three times a year in English-language, provides scientists and engineers throughout the world for the exchange and dissemination of theoretical and ...