Structured Bipartite Graph Ensemble Clustering

Chen Wang, Feng Hou, Yi Wang, Ruili Wang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Conventional bipartite graph-based cluster ensembles usually compute a weight matrix from a base clustering result to represent the similarities between the samples and clusters. These weight matrices are concatenated or averaged as a bipartite weight matrix with which a bipartite graph is created, and then graph-based partition techniques are used to partition the samples in the bipartite graph as the final clustering result. However, such methods may suffer from unreliable clusterings and it is difficult to find an explicit cluster structure from the bipartite graph since the clusters in the base clusterings are usually diverse. In this paper, we propose a novel Structured Bipartite Graph Ensemble Learning (SBGEC) for cluster ensembles. To address these issues, SBGEC constructs a sample-cluster similarity matrix for each base clustering and introduces a weight matrix rearrangement technique to optimize cluster correspondences. Multiple bipartite graphs are generated using these matrices, and a structured bipartite graph is learned to combine the original graphs while maintaining a clear cluster structure. Experimental results on synthetic and real-world datasets demonstrate the superior performance of SBGEC compared to state-of-the-art clustering techniques.

Original languageEnglish
Title of host publicationProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024
PublisherAssociation for Computing Machinery, Inc
ISBN (Electronic)9798400712739
DOIs
Publication statusPublished - 28 Dec 2024
Externally publishedYes
Event6th ACM International Conference on Multimedia in Asia, MMAsia 2024 - Auckland, New Zealand
Duration: 3 Dec 20246 Dec 2024

Publication series

NameProceedings of the 6th ACM International Conference on Multimedia in Asia, MMAsia 2024

Conference

Conference6th ACM International Conference on Multimedia in Asia, MMAsia 2024
Country/TerritoryNew Zealand
CityAuckland
Period3/12/246/12/24

Keywords

  • Clustering
  • Ensemble Clustering
  • Structure Learning

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction

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