The new window density function for efficient evolutionary unsupervised clustering

Evolutionary clustering is a recent trend in cluster analysis that has the potential to yield high partitioning accuracy results. Traditional evolutionary techniques applied in clustering are typically hindered by the high cost involved in the computation of the objective function. In this paper, the authors proposed a novel objective function that can provide fitness function values in sub-linear time. Next an evolutionary scheme was developed to evolve cluster solutions and demonstrate how the number of clusters can be estimated from the final result. Finally, by employing real world datasets, the high quality clustering results that this scheme can provide was shown.