# Multi-View Multiple Clustering

## TL;DR

This paper introduces a novel multi-view multiple clustering (MVMC) algorithm that leverages both individual and shared information in multi-view data to generate diverse, high-quality clusterings, outperforming existing methods.

## Contribution

The paper proposes a new MVMC algorithm that combines multi-view self-representation learning, HSIC-based redundancy reduction, and matrix factorization to improve clustering diversity and quality.

## Key findings

- MVMC generates diverse high-quality clusterings.
- MVMC outperforms state-of-the-art methods in empirical tests.
- Extension to multi-view co-clustering (MVMCC) enhances clustering performance.

## Abstract

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.