A Modified Cuckoo Search Algorithm for Data Clustering

Clustering of data is one of the necessary data mining techniques, where similar objects are grouped in the same cluster. In recent years, many nature-inspired clustering techniques have been proposed, which have led to some encouraging results. This paper proposes a modified cuckoo search (MoCS) algorithm. In this work, an attempt has been made to balance the exploration of the cuckoo search (CS) algorithm and to increase the potential of the exploration to avoid premature convergence. This algorithm is tested using 15 benchmark test functions and is proven as an efficient algorithm in comparison to the CS algorithm. Further, this method is compared with well-known nature-inspired algorithms such as ant colony optimization (ACO), artificial bee colony (ABC), particle swarm optimization (PSO), particle swarm optimization with age group topology (PSOAG), and CS algorithm for clustering of data using six real datasets. The experimental results indicate that the MoCS algorithm achieves better results as compared to other algorithms in finding optimal cluster centers.