Evidential weighted multi-view clustering. (English) Zbl 1518.68334
Denœux, Thierry (ed.) et al., Belief functions: theory and applications. 6th international conference, BELIEF 2021, Shanghai, China, October 15–19, 2021. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 12915, 22-32 (2021).
Summary: Generally, the data to be clustered are from one single view. In real clustering applications, sometimes the data are insufficient so that it is difficult to learn an ideal cluster model. In such cases, multi-view data can be taken into consideration in the clustering task. However, the inconsistency cross views may increase the cluster uncertainty. In this research, a new clustering method for multi-view object data, called MvWECM (Multi-view Weighted Evidential \(C\)-Means) is introduced in the framework of belief functions. The proposed method can take consistency and diversity cross each view into account by incorporating the concept of view weights to measure the importance of each view. An objective function is defined to look for the best credal partitions over the different views. Experimental results on generated and UCI data sets show the advantage of the proposed method.
For the entire collection see [Zbl 1507.68028].
For the entire collection see [Zbl 1507.68028].
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
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