Multi-View Fuzzy Clustering with Minimax Optimization for Effective Clustering of Data from Multiple Sources

TL;DR

This paper introduces MinimaxFCM, a novel multi-view fuzzy clustering method that minimizes maximum disagreements across views using minimax optimization, automatically learns view weights, and outperforms existing methods on real-world datasets.

Contribution

The paper proposes MinimaxFCM, a new multi-view fuzzy clustering algorithm that effectively integrates multiple data representations through minimax optimization and automatic view weighting.

Findings

MinimaxFCM outperforms existing multi-view clustering methods in accuracy.

The method automatically learns weights for different views.

Experimental results on nine datasets validate its effectiveness.

Abstract

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real world image and document data sets have been conducted. We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.