Title: A survey on partitional clustering using single-objective metaheuristic approach

Authors: Preeti Pragyan Mohanty; Subrat Kumar Nayak; Usha Manasi Mohapatra; Debahuti Mishra

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-30, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-30, Odisha, India ' Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-30, Odisha, India ' Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar-30, Odisha, India

Abstract: Clustering is one of the important functions of data mining, which is used to analyse a large amount of data. It groups these set of data according to some similarity property such that data within the cluster are similar to each other and data between the clusters are dissimilar to each other. To obtain an optimal clustering result with the help of an optimisation algorithm is an emerging trend in data mining. The partitional clustering is one of the popularly used types of clustering algorithm. These algorithms often land in local optimum and number of clusters needs to be predefined. To encounter the above problem, optimisation algorithms such as metaheuristic algorithms are used as a suitable problem-solving paradigm. This paper presents an overview of single-objective metaheuristic algorithms used for partitional clustering problem and their applications. This paper even presents the research issues which can be dealt with in future.

Keywords: partitional clustering; metaheuristic approach; evolutionary algorithm; swarm optimisation algorithm; physics-inspired algorithm; bio-inspired algorithm.

DOI: 10.1504/IJICA.2019.103395

International Journal of Innovative Computing and Applications, 2019 Vol.10 No.3/4, pp.207 - 226

Received: 01 Apr 2019
Accepted: 13 May 2019
Published online: 05 Nov 2019 *

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