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 article, 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.

中文翻译：

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Geono-Cluster：生物学家的交互式视觉聚类分析


生物学家经常进行聚类分析，从数据实例和属性中得出有意义的模式、关系和结构。尽管聚类在生物学家的数据探索中发挥着关键作用，但生物学家需要付出不小的努力才能使用现有工具在数据中找到最佳分组。目前，视觉聚类分析要么以编程方式执行，要么通过许多工具中的菜单和对话框执行，这需要通过几个试错步骤进行参数调整。在本文中，我们介绍了 Geono-Cluster，这是一种新颖的可视化分析工具，旨在支持没有接受过正式数据科学培训的生物学家进行聚类分析。 Geono-Cluster 使生物学家能够通过直观地展示小数据实例样本的预期聚类输出，将他们的领域专业知识应用到聚类结果中。然后系统预测用户的意图并生成潜在的聚类结果。我们的研究遵循设计研究协议来得出生物学家的任务和要求，设计系统，并与专家在他们自己的数据集上评估系统。我们与六位生物学家的研究结果提供了初步证据，表明 Geono-Cluster 使生物学家能够创建、完善和评估聚类结果，以有效分析他们的数据并获得数据驱动的见解。最后，我们讨论了我们的研究的经验教训和影响。