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Multi-view document clustering via ensemble method

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Abstract

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.

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  1. http://www.imdb.org

  2. http://www.cs.umd.edu/%7Esen/lbc-proj/data/citeseer.tgz

  3. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/

  4. http://www.cs.umd.edu/%7Esen/lbc-proj/data/cora.tgz

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Correspondence to Syed Fawad Hussain.

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Hussain, S.F., Mushtaq, M. & Halim, Z. Multi-view document clustering via ensemble method. J Intell Inf Syst 43, 81–99 (2014). https://doi.org/10.1007/s10844-014-0307-6

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