Hashtag Discernability - Competitiveness Study of Graph Spectral and Other Clustering Methods

Abstract

Spectral clustering methods are claimed to possess ability to represent clusters of diverse shapes, densities etc. They constitute an approximation to graph cuts of various types (plain cuts, normalized cuts, ratio cuts). They are applicable to unweighted and weighted similarity graphs. We perform an evaluation of these capabilities for clustering tasks of increasing complexity.