Title: Building a hybridised meta-heuristic optimisation algorithm for efficient cluster analysis

Authors: D. Pradeep Kumar; B.J. Sowmya; Anita Kanavalli; Varun Cornelio; Jaison Pravith Dsouza; Wasim Memon; P. Prashanth

Addresses: Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, Bangalore, India

Abstract: Nature-inspired algorithms are a relatively recent field of meta-heuristics introduced to optimise the process of clustering unlabelled data. In recent years, hybridisation of these algorithms has been pursued to combine the best of multiple algorithms for more efficient clustering and overcoming their drawbacks. In this paper, we discuss a novel hybridisation concept where we combine the exploration and exploitation processes of the vanilla bat and vanilla whale algorithm to develop a hybrid meta-heuristic algorithm. We test this algorithm against the existing vanilla meta-heuristic algorithms, including the vanilla bat and whale algorithm. These tests are performed on several single objective CEC functions to compare convergence speed to the minima coordinates. Additional tests are performed on several real-life and artificial clustering datasets to compare convergence speeds and clustering quality. Finally, we test the hybrid on real-world cases with unlabelled clustering data, namely a credit card fraud detection dataset, and a COVID-19 diagnosis dataset, and end with a discussion on the significance of the work, its limitations and future scope.

Keywords: nature-inspired algorithms; cluster analysis; vanilla whale; vanilla bat; optimisation algorithms.

DOI: 10.1504/IJBIDM.2023.127349

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.1/2, pp.170 - 222

Received: 11 Sep 2021
Accepted: 23 Nov 2021
Published online: 30 Nov 2022 *