  Multiple clustering aims at exploring alternative clusterings to organize the
data into meaningful groups from different perspectives. Existing multiple
clustering algorithms are designed for single-view data. We assume that the
individuality and commonality of multi-view data can be leveraged to generate
high-quality and diverse clusterings. To this end, we propose a novel
multi-view multiple clustering (MVMC) algorithm. MVMC first adapts multi-view
self-representation learning to explore the individuality encoding matrices and
the shared commonality matrix of multi-view data. It additionally reduces the
redundancy (i.e., enhancing the individuality) among the matrices using the
Hilbert-Schmidt Independence Criterion (HSIC), and collects shared information
by forcing the shared matrix to be smooth across all views. It then uses matrix
factorization on the individual matrices, along with the shared matrix, to
generate diverse clusterings of high-quality. We further extend multiple
co-clustering on multi-view data and propose a solution called multi-view
multiple co-clustering (MVMCC). Our empirical study shows that MVMC (MVMCC) can
exploit multi-view data to generate multiple high-quality and diverse
clusterings (co-clusterings), with superior performance to the state-of-the-art
methods.
