Multi-view document clustering via ensemble method

Syed Fawad Hussain*, Muhammad Mushtaq, Zahid Halim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Multi-view clustering has become an important extension of ensemble clustering. In multi-view clustering, we apply clustering algorithms on different views of the data to obtain different cluster labels for the same set of objects. These results are then combined in such a manner that the final clustering gives better result than individual clustering of each multi-view data. Multi view clustering can be applied at various stages of the clustering paradigm. This paper proposes a novel multi-view clustering algorithm that combines different ensemble techniques. Our approach is based on computing different similarity matrices on the individual datasets and aggregates these to form a combined similarity matrix, which is then used to obtain the final clustering. We tested our approach on several datasets and perform a comparison with other state-of-the-art algorithms. Our results show that the proposed algorithm outperforms several other methods in terms of accuracy while maintaining the overall complexity of the individual approaches.

Original languageEnglish
Pages (from-to)81-99
Number of pages19
JournalJournal of Intelligent Information Systems
Issue number1
Publication statusPublished - Aug 2014


  • Affinity matrix
  • Ensemble clustering
  • Multi-view clustering
  • Similarity matrices

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence


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