Multilevel Diffusion Based Spectral Graph Clustering
Informazioni aggiuntive
Autori
Curatori
Tipo
Atti di conferenza
Anno
in uscita
Lingua
Inglese
Abstract
Spectral clustering and kernel k-means are ubiquitous methods for dividing non-linearly separable data into distinct groups. Spectral methods, while effective in partitioning non-convex spaces, are computationally intensive as they involve eigenvector computations. Conversely, kernel k-means maps data to higher dimensions and circumvents the need for this costly computation, with a weighted variant of it being mathematically equivalent to spectral clustering. This work extends this
equivalence to diffusion based spectral methods and introduces the Multilevel Diffusion Clustering (MDC) algorithm. MDC leverages diffusion principles to minimize the normalized cut, adds
flexibility through a diffusion parameter, and retrieves high-qualitypartitions in a multilevel fashion without computing eigenpairs. Our numerical examples and comparative results with modern
multilevel graph clustering packages reveal that the proposed method can improve the clustering of graphs both in terms of balanced cut criteria and classification accuracy.
Mese
settembre
Editore
IEEE
Pagina inizio
1
Pagina fine
6
Nome conferenza
28th Annual IEEE High Performance Extreme Computing Virtual Conference
Parole chiave
Graph clustering, kernel k-means, spectral clustering, multilevel framework
