Deep incomplete multi-view multiple clusterings

Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations


Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on the predefined clustering criterion, but devising such a criterion that captures what users need is difficult. Due to the multiplicity of multi-view data, we can have meaningful alternative clusterings. In addition, the incomplete multi-view data problem is ubiquitous in real world but has not been studied for multiple clusterings. To address these issues, we introduce a deep incomplete multi-view multiple clusterings (DiMVMC) framework, which achieves the completion of data view and multiple shared representations simultaneously by optimizing multiple groups of decoder deep networks. In addition, it minimizes a redundancy term to simultaneously control the diversity among these representations and among parameters of different networks. Next, it generates an individual clustering from each of these shared representations. Experiments on benchmark datasets confirm that DiMVMC outperforms the state-of-the-art competitors in generating multiple clusterings with high diversity and quality.
Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Data Mining (ICDM)
Number of pages10
ISBN (Print)9781728183169
StatePublished - Nov 2020

Bibliographical note

KAUST Repository Item: Exported on 2021-03-01


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