A Physically Inspired Clustering Algorithm: to Evolve Like Particles
share
Clustering analysis is a method to organize raw data into categories based on a measure of similarity. It has been successfully applied to diverse fields from science to business and engineering. By endowing data points with physical meaning like particles in the physical world and then leaning their evolving tendency of moving from higher to lower potentials, data points in the proposed clustering algorithm sequentially hop to the locations of their transfer points and gather, after a few steps, at the locations of cluster centers with the locally lowest potentials, where cluster members can be easily identified. The whole clustering process is simple and efficient, and can be performed either automatically or interactively, with reliable performances on test data of diverse shapes, attributes, and dimensionalities.
READ FULL TEXT