  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
%uses Hilbert-Schmidt Independence Criterion (HSIC) to 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.
