An improved transient search optimisation technique for effective data clustering

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.