Research of nonparametric density estimation algorithms by applying clustering methods

Abstract

One of the ways to improve the accuracy of probability density estimation is multi-mode density treating as the mixture of single-mode one. In this paper we offer to use data clustering in the first place and to estimate density in every cluster separately. To objectively compare the performance, Monte Carlo approximation is used. While using various methods to evaluate the accuracy of probability density estimations we tried to use clustered and not clustered data. In this paper we also tried to reveal the usefulness of using clustering for data generated by single-mode and multi-mode distributions.