  In complex multivariate data sets, different features usually include diverse
associations with different variables, and different variables are associated
within different regions. Therefore, exploring the associations between
variables and voxels locally becomes necessary to better understand the
underlying phenomena. In this paper, we propose a co-analysis framework based
on biclusters, which are two subsets of variables and voxels with close
scalar-value relationships, to guide the process of visually exploring
multivariate data. We first automatically extract all meaningful biclusters,
each of which only contains voxels with a similar scalar-value pattern over a
subset of variables. These biclusters are organized according to their variable
sets, and biclusters in each variable set are further grouped by a similarity
metric to reduce redundancy and support diversity during visual exploration.
Biclusters are visually represented in coordinated views to facilitate
interactive exploration of multivariate data based on the similarity between
biclusters and the correlation of scalar values with different variables.
Experiments on several representative multivariate scientific data sets
demonstrate the effectiveness of our framework in exploring local relationships
among variables, biclusters and scalar values in the data.
