data-based learning for probability density functions

In this study, we discuss the statistical perspective of clustering data thathave unknown probability distribution functions. a jackknife entropy-based clusteringalgorithm is introduced and utilized for clustering data. in order to this goal,we presented the renyi entropy with accomplishing the kullback-leibler divergence.experiments on real-world data show that our method is effective in finding good clustering.