A New Spectral Clustering Algorithm

TL;DR

This paper introduces a novel spectral clustering algorithm that identifies natural gaps in eigenvector components to improve clustering performance, especially in complex networks and climate data analysis.

Contribution

The paper proposes a new spectral clustering method based on eigenvector gap analysis, outperforming existing spectral methods in certain network benchmarks and effectively analyzing climate variability modes.

Findings

Outperforms other spectral clustering methods in benchmark tests.

Successfully identifies different flavors of El Nino Southern Oscillation.

Demonstrates effectiveness in real-world climate data analysis.

Abstract

We present a new clustering algorithm that is based on searching for natural gaps in the components of the lowest energy eigenvectors of the Laplacian of a graph. In comparing the performance of the proposed method with a set of other popular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the context of the Lancichinetti-Fortunato-Radicchi (LFR) Benchmark for undirected weighted overlapping networks, we find that the new method outperforms the other spectral methods considered in certain parameter regimes. Finally, in an application to climate data involving one of the most important modes of interannual climate variability, the El Nino Southern Oscillation phenomenon, we demonstrate the ability of the new algorithm to readily identify different flavors of the phenomenon.