A hybrid data clustering approach based on improved cat swarm optimization and K-harmonic mean algorithm

Article type: Research Article

Authors: Kumar, Yugal^{; *} | Sahoo, G.

Affiliations: Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India. E-mails: [email protected], [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Clustering is a process to discover unseen patterns in a given set of objects. Objects belonging to the same pattern are homogenous in nature while they are heterogeneous in other patterns. In this paper, a hybrid data clustering algorithm comprising of improved cat swarm optimization (CSO) and K-harmonic means (KHM) is proposed to solve the clustering problem. The proposed algorithm exhibits strengths of both the mentioned algorithms, it is named as improved CSOKHM (ICSOKHM). The performance of the proposed algorithm is evaluated using seven datasets and is compared with existing algorithms like KHM, PSO, PSOKHM, ACA, ACAKHM, GSAKHM and CSO. The experimental results demonstrate that the proposed algorithm not only improves the convergence speed of CSO algorithm but also prevents KHM algorithm from running into local optima.

Keywords: Cat swarm optimization, data clustering, K-harmonic means, gravitational search algorithm, particle swarm optimization

DOI: 10.3233/AIC-150677

Journal: AI Communications, vol. 28, no. 4, pp. 751-764, 2015