Title: Clustering ensemble by clustering selected weighted clusters

Authors: Arko Banerjee; Suvendu Chandan Nayak; Chhabi Rani Panigrahi; Bibudhendu Pati

Addresses: College of Engineering and Management, Kolaghat Thermal Power Plant Township, Kolaghat, East Midnapore, West Bengal, 721171, India; Biju Patnaik University of Technology, Chhend Colony, Rourkela, Odisha, 769004, India ' Silicon Institute of Technology, Silicon Hills, Near DLF Cybercity, Patia, Bhubaneswar, Odisha, 751024, India ' Ramadevi Women's University, Bhoinagar P.O., Bhubaneswar, Odisha, 751022, India ' Ramadevi Women's University, Bhoinagar P.O., Bhubaneswar, Odisha, 751022, India

Abstract: Due to the fact that no single clustering approach is capable of producing the optimal result for any given data, the notion of clustering ensembles has emerged, which attempts to extract a novel and robust consensus clustering from a given ensemble of base clusterings of the data. While forming the consensus, weights can be assigned to the base clusterings or their constituent clusters to prioritise those that accurately represent the underlying structure of the data. In this paper, we present a novel method of cluster selection from base clusterings and subsequently merging selected clusters into desired number of clusters in order to build a high-quality consensus clustering without gaining access to the internal distribution of data points. The method has been shown to work well with a wide range of data and to be better than many well-known clustering methods.

Keywords: clustering ensemble; weighted clustering; entropy; cluster selection.

DOI: 10.1504/IJCSE.2024.137284

International Journal of Computational Science and Engineering, 2024 Vol.27 No.2, pp.159 - 166

Received: 12 May 2022
Received in revised form: 09 Oct 2022
Accepted: 13 Oct 2022
Published online: 11 Mar 2024 *

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