Multiway Spectral Clustering: A Margin-Based Perspective

Multiway Spectral Clustering: A Margin-Based Perspective

Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is “relaxed” into a tractable eigenvector problem, and in which the relaxed solution is subsequently “rounded” into an approximate discrete solution to the original problem. In this paper we present a novel margin-based perspective on multiway spectral clust...

Graph Clustering Using Multiway Ratio

First-principles multiway spectral partitioning of graphs

We consider the minimum-cut partitioning of a graph into more than two parts using spectral methods. While there exist well-established spectral algorithms for this problem that give good results, they have traditionally not been well motivated. Rather than being derived from first principles by minimizing graph cuts, they are typically presented without direct derivation and then proved after ...

SACOC: A Spectral-Based ACO Clustering Algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that se...

A New Anticorrelation-Based Spectral Clustering Formulation

This paper introduces the Spectral Clustering Equivalence (SCE) algorithm which is intended to be an alternative to spectral clustering (SC) with the objective to improve both speed and quality of segmentation. Instead of solving for the spectral decomposition of a similarity matrix as in SC, SCE converts the similarity matrix to a columncentered dissimilarity matrix and searches for a pair of ...