Collaborative multi-view K-means clustering
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Due to the huge diversity and heterogeneity of data coming from websites and new technologies, data contents can be better represented by multiple representations for taking advantage of their complementary characteristics efficiently. This paper presents and discusses a new approach for collaborative multi-view clustering based on K-means hypothesis but modified in different ways. Our solution seeks to find a consensus solution from multiple representations by exploiting information from each of them to improve the performance of classical clustering system. To exhibit its effectiveness, the proposed approach is evaluated on two image datasets having different sizes and features. The obtained results reconfirm that multi-view clustering gives performant results and shows that our proposal outperforms mono-view clustering and also several other algorithms in the literature in terms of accuracy, purity and normalized mutual information.
KeywordsMulti-view clustering K-means clustering Collaborative clustering
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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