Biclusters Based Visual Exploration of Multivariate Scientific Data

Abstract

This paper proposes a co-analysis framework based on biclusters, i.e., two subsets of variables and voxels with close scalar-value relationships, to guide the visual exploration process of 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 further grouped by a similarity metric to reduce redundancy and encourage diversity during visual exploration. Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data from the similarity between biclusters and the correlation of scalar values with different variables. Experiments demonstrate the effectiveness of our framework in exploring local relationships among variables, biclusters and scalar values in the data.