# A Co-analysis Framework for Exploring Multivariate Scientific Data

## TL;DR

This paper introduces a bicluster-based co-analysis framework for exploring complex multivariate scientific data, enabling interactive visualization of local variable-voxel relationships and scalar-value patterns.

## Contribution

It presents an automatic bicluster extraction and organization method to facilitate diverse and efficient visual exploration of multivariate data.

## Key findings

- Effective in revealing local relationships among variables and voxels
- Supports diverse visual exploration through bicluster grouping
- Demonstrated success on multiple scientific datasets

## Abstract

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.