Multi-view document clustering via ensemble method |
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Authors: | Syed Fawad Hussain Muhammad Mushtaq Zahid Halim |
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Affiliation: | 1. Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, 23460, Topi, Pakistan
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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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