Abstract

High-dimensional data arise naturally in a lot of domains, and have regularly presented a great confront for usual data mining techniques. This work, take a novel perspective on the problem of data points (data in the orientation of contain points) in clustering large-dimensional data. The planned methodology known as root mappings and neighbor clustering, that takes as input measures of correspondence between pairs of information points. Real-valued data points are exchanged between data points until a high-quality set of patterns and corresponding clusters gradually emerges. To validate our theory by demonstrating that data points is a high-quality measure of point centrality within a high-dimensional information cluster, and by proposing several clustering algorithms, showing that main data points can be used effectively as cluster prototypes or as guides during the search for centroid-based cluster patterns. Experimental results demonstrate the good performance of our proposed algorithms in manifold settings, mainly focused on large quantities of overlapping noise. The proposed methods are modified mostly for detecting approximately hyper spherical clusters and need to be extended to properly handle clusters of arbitrary shapes.