Data clustering using bacterial foraging optimization

TL;DR: Experimental results show that the proposed algorithm is an effective clustering technique and can be used to handle data sets with various cluster sizes, densities and multiple dimensions.

Abstract: Clustering divides data into meaningful or useful groups (clusters) without any prior knowledge. It is a key technique in data mining and has become an important issue in many fields. This article presents a new clustering algorithm based on the mechanism analysis of Bacterial Foraging (BF). It is an optimization methodology for clustering problem in which a group of bacteria forage to converge to certain positions as final cluster centers by minimizing the fitness function. The quality of this approach is evaluated on several well-known benchmark data sets. Compared with the popular clustering method named k-means algorithm, ACO-based algorithm and the PSO-based clustering technique, experimental results show that the proposed algorithm is an effective clustering technique and can be used to handle data sets with various cluster sizes, densities and multiple dimensions.