Incremental Minimax Optimization based Fuzzy Clustering for Large Multi-view Data

TL;DR

This paper introduces IminimaxFCM, an incremental fuzzy clustering method that effectively handles large multi-view datasets by integrating multiple views through minimax optimization, improving clustering accuracy.

Contribution

The paper proposes a novel incremental multi-view fuzzy clustering algorithm using minimax optimization, addressing the challenge of large multi-view data analysis.

Findings

IminimaxFCM outperforms existing incremental fuzzy clustering methods in accuracy.

The approach effectively integrates multiple views for better clustering.

Experimental results demonstrate its potential for large multi-view data analysis.

Abstract

Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all the chunks. However, most of the incremental approaches are used for single view data. As large multi-view data generated from multiple sources becomes prevalent nowadays, there is a need for incremental clustering approaches to handle both large and multi-view data. In this paper we propose a new incremental clustering approach called incremental minimax optimization based fuzzy clustering (IminimaxFCM) to handle large multi-view data. In IminimaxFCM, representatives with multiple views are identified to represent each cluster by integrating multiple complementary views using minimax optimization. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IminimaxFCM are provided. Experimental studies on several real world multi-view data sets have been conducted. We observed that IminimaxFCM outperforms related incremental fuzzy clustering in terms of clustering accuracy, demonstrating the great potential of IminimaxFCM for large multi-view data analysis.