  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 the implications of our
study.
