Co-clustering of multi-view datasets

Syed Fawad Hussain*, Shariq Bashir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


In many clustering problems, we have access to multiple sources of data representing different aspects of the problem. Each of these data separately represents an association between entities. Multi-view clustering involves integrating clustering information from these heterogeneous sources of data and has been shown to improve results over a single-view clustering. On the other hand, co-clustering has been widely used as a technique to improve clustering results on a single view by exploiting the duality between objects and their attributes. In this paper, we propose a multi-view clustering setting in the context of a co-clustering framework. Our underlying assumption is that similarity values generated from the individual data can be transferred from one view to the other(s) resulting in a better clustering of the data. We provide empirical evidence to show that this framework results in a better clustering accuracy than those obtained from any of the single views, tested on different datasets.

Original languageEnglish
Pages (from-to)545-570
Number of pages26
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 1 Jun 2016

Bibliographical note

Publisher Copyright:
© 2015, Springer-Verlag London.


  • Co-clustering
  • Ensemble clustering
  • Multi-view clustering
  • Similarity measure
  • Transfer learning

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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