  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.
