ENTROPY-BASED SUPERVISED LEARNING FOR PROBABILITY DENSITY FUNCTIONS

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. This paper proposes a new unsupervised learning algorithm called the jackknife entropy-based clustering algorithm for grouping families of probability density functions (pdfs). To this end, the Renyi entropy with accomplishing the KullbackLeibler divergence is utilized. Using the aforementioned strategy, the clustering method is examined through some simulation studies. Their advantages are provided and finally, a real data set are analyzed.

Keywords:

probability density function , clustering , jackknife entropy , Statistical