Geono-Cluster: Interactive Visual Cluster Analysis for Biologists
SUMMARY
Biologists often perform clustering analysis to derive meaningful patterns, relationships, and
structures from data instances
and attributes. Though clustering plays a pivotal role in biologists’ data exploration, it takes
non-trivial efforts for biologists to find the best
grouping in their data using existing tools. Visual cluster analysis is currently performed either
programmatically or through menus and
dialogues in many tools, which require parameter adjustments over several steps of trial-and-error.
In this paper, we introduce
Geono-Cluster, a novel visual analysis tool designed to support cluster analysis for biologists who
do not have formal data science
training. Geono-Cluster enables biologists to apply their domain expertise into clustering results
by visually demonstrating how their
expected clustering outputs should look like with a small sample of data instances. The system then
predicts users’ intentions and
generates potential clustering results. Our study follows the design study protocol to derive
biologists’ tasks and requirements, design the
system, and evaluate the system with experts on their own dataset. Results of our study with six
biologists provide initial evidence that
Geono-Cluster enables biologists to create, refine, and evaluate clustering results to effectively
analyze their data and gain data-driven
insights. At the end, we discuss lessons learned and implications of our study