BAT-CLARA: BAT-inspired algorithm for Clustering LARge Applications

Bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. Clustering remains one of the most difficult challenges in data mining. In this paper, an overview of literature methods is undertaken followed by the presentation of a new algorithm called BAT-CLARA for clustering large data sets. It is based on bat behavior and k-medoids partitioning. The new technique is compared to the well-know partitioning algorithms PAM, CLARA, CLARANS and CLAM, a recent algorithm found in the literature. Experimental results show that, for the same tested datasets, BAT-CLARA is more effective and more efficient than previous clustering methods.