Unsupervised Clustering on Signed Graphs with Unknown Number of Clusters

We consider the problem of unsupervised clustering on signed graphs, i.e., graphs with positive and negative edge weights. Motivated by signed cut minimization, we propose an optimization problem that minimizes the total variation of the cluster labels subject to constraints on the cluster size, aug...

Full description

Saved in:
Bibliographic Details
Published in2020 28th European Signal Processing Conference (EUSIPCO) pp. 1060 - 1064
Main Authors Dittrich, Thomas, Matz, Gerald
Format Conference Proceeding
LanguageEnglish
Published Eurasip 24.01.2021
Subjects
Clustering algorithms
Partitioning algorithms
Signal processing
Signal processing algorithms
Simulation
Upper bound
Online AccessGet full text

Cover

More Information
Summary:We consider the problem of unsupervised clustering on signed graphs, i.e., graphs with positive and negative edge weights. Motivated by signed cut minimization, we propose an optimization problem that minimizes the total variation of the cluster labels subject to constraints on the cluster size, augmented with a regularization that prevents clusters consisting of isolated nodes. We estimate the unknown number of clusters by tracking the change of total variation with successively increasing putative cluster numbers. Simulation results indicate that our method yields excellent results for moderately unbalanced graphs.
ISSN:2076-1465
DOI:10.23919/Eusipco47968.2020.9287424