Title: An improved transient search optimisation technique for effective data clustering

Authors: Prateek Thakral; Pardeep Kumar; Yugal Kumar

Addresses: Jaypee University of Information Technology, Solan, Himachal Pradesh, India ' Jaypee University of Information Technology, Solan, Himachal Pradesh, India ' School of Technology Management and Engineering, Narsee Monjee Institute of Management Studies (NMIMS) (Chandigarh Campus), Sarangpur, Chandigarh, India

Abstract: Data clustering is a fundamental task in the field of machine learning which involves the partitioning of the datasets into meaningful groups. The traditional clustering algorithms often struggle with issues such as initial centroid sensitivity, slow convergence, and local optima trap. On the other side, meta-heuristic algorithms consist of innovative paradigms to handle these issues. Hence, this work introduces a new meta-heuristic algorithm, called transient search optimisation (TSO) to alleviate the issues of traditional clustering algorithms. Further, some enhancements are included in TSO algorithm to generate more optimal results. These improvements aim to make TSO more reliable for data clustering problems. The efficiency of the TSO is evaluated over benchmark datasets and results are compared using intra cluster distance, accuracy rate and detection rate parameters. The average accuracy rate and average detection rate of the proposed TSO algorithm are 6.94% and 6.48% higher respectively as compared to other algorithms.

Keywords: data clustering; meta-heuristic algorithms; transient search optimisation; partitional clustering.

DOI: 10.1504/IJGUC.2025.146283

International Journal of Grid and Utility Computing, 2025 Vol.16 No.3, pp.279 - 295

Received: 22 May 2024
Accepted: 20 Aug 2024
Published online: 15 May 2025 *

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