Author/Authors :
Kumar, Vijay Department of Computer Science and Engineering - Thapar University, Patiala, India , Kumar Chhabra, Jitender Department of Computer Engineering - National Institute of Technology, Kurukshetra, India , Kumar, Dinesh Department of Computer Science and Engineering - Guru Jambheshwar University of Science and Technology, Haryana, India
Abstract :
The main challenges of clustering techniques are to tune the initial cluster centres and to avoid the
solution being trapped in the local optima. In this paper, a new metaheuristic algorithm, Differential
Search (DS), is used to solve these problems. The DS explores the search space of the given dataset to
find the near-optimal cluster centres. The cluster centre-based encoding scheme is used to evolve the
cluster centres. The proposed DS-based clustering technique is tested over four real-life datasets. The
performance of DS-based clustering is compared with four recently developed metaheuristic techniques.
The computational results are encouraging and demonstrate that the DS-based clustering provides better
values in terms of precision, recall and G-Measure.
