Reductive Clustering of High-dimensional Data

Abstract

A method of nonparametric clustering of Big Data based on histogram analysis of images in the feature space is proposed. The method allows you to localize cluster zones and cluster centers in subspaces of the feature space without using distance metrics. The proposed method bypasses the “curse of dimensionality” and is suitable for analyzing both numerical and categorical high-dimensional data.