An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration

TL;DR

This study investigates how analysts approach grouping and spatial organization tasks in high-dimensional data exploration, revealing common strategies and providing design recommendations for improved visualization tools.

Contribution

It presents a detailed study of analyst behaviors in organizing high-dimensional data and offers design insights to enhance exploration tools using grouping and spatial operations.

Findings

Identified common approaches to data organization

Analyzed interactions performed during exploration

Proposed design recommendations for visualization tools

Abstract

How do analysts think about grouping and spatial operations? This overarching question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.