Spectral clustering using compactly supported graph building

Abstract

Clustering techniques demand on suitable models of data structures to infer the main samples patterns. Nonetheless, detection of data structures becomes a difficult task when dealing with nonlinear data relationships and complex distributions. Here, to support clustering tasks, we introduce a new graph building strategy based on a compactly supported kernel technique. Thus, our approach makes relevant pairwise sample relationships by finding a sparse kernel matrix that codes the main sample connections. Clustering performance is assessed on synthetic and real-world data sets. Obtained results show that the proposed method enhances the data interpretability and separability by revealing relevant data relationships into a graph-based representation.