Interpretable Multi-View Clustering

TL;DR

This paper introduces the first interpretable multi-view clustering framework that combines feature embedding, pseudo-labels, and decision trees to provide transparent clustering decisions while maintaining competitive accuracy.

Contribution

It presents a novel, interpretable clustering method for multi-view data that integrates feature learning and decision tree optimization, filling a significant research gap.

Findings

Provides transparent clustering process for multi-view data

Achieves performance comparable to state-of-the-art methods

First to design an interpretable framework for multi-view clustering

Abstract

Multi-view clustering has become a significant area of research, with numerous methods proposed over the past decades to enhance clustering accuracy. However, in many real-world applications, it is crucial to demonstrate a clear decision-making process-specifically, explaining why samples are assigned to particular clusters. Consequently, there remains a notable gap in developing interpretable methods for clustering multi-view data. To fill this crucial gap, we make the first attempt towards this direction by introducing an interpretable multi-view clustering framework. Our method begins by extracting embedded features from each view and generates pseudo-labels to guide the initial construction of the decision tree. Subsequently, it iteratively optimizes the feature representation for each view along with refining the interpretable decision tree. Experimental results on real datasets demonstrate that our method not only provides a transparent clustering process for multi-view data but also delivers performance comparable to state-of-the-art multi-view clustering methods. To the best of our knowledge, this is the first effort to design an interpretable clustering framework specifically for multi-view data, opening a new avenue in this field.