Data clustering using bacterial colony optimization with particle swarm optimization

grouping a set of data items based on similarity is called data clustering. The bacterial colony optimization (BCO) algorithm is a distinguished method. Lately, it is used to apply a problem of data clustering. But, the convergence rate of BCO is very low. In this present research paper, the particle swarm optimization (PSO) is applied to improve the BCO convergence rate and improve the effectiveness of the problem. The investigational effects exhibit that the proposed BCO-PSO formed higher performance when compared with some benchmark data clustering algorithms.