  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.
