Data-Based Learning for Probability Density Functions

In this study, we discuss the statistical perspective of clustering data that have unknown probability distribution functions. A jackknife entropy-based clustering algorithm 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.