CoCo: Comparative and Collaborative Visual Analysis of Clustering and Co-clustering Ensembles

集群自动对复杂数据项进行分组，并基于数据相似性创建抽象。然而，计算实体相似性的不同标准以及不同的聚类算法是可用的，所有这些都产生不同的实体分组。由此产生的集群集合的多样性通常被试图最小化，然而，我们在多样性中看到了价值和机会。只有人类专家才能判断结果的质量，并在其中反映的多种真理中进行选择。他们可以考虑各个用例的细节，并根据不同的结果组合成一个总体分组。多个专家的协作可能值得称赞，以涵盖更广泛的专业知识。为了支持人类专家，我们提出了新的方法来可视化和可视地比较这些聚类结果的集合。我们的方法超越了最先进的视觉比较方法，解决了关于集群结构特征、它们在其他数据中的上下文嵌入和协作分析的更复杂场景。我们不仅要研究项目的简单划分，而且要研究嵌入到上下文网络结构中的重叠组和集群。一个具体的焦点是联合集群，它还沿着实体构建数据变量的结构，从而建立更容易理解的分组。例如，可以从一组科学文献中确定不同的主题，并与所包括的术语一起形成共同的集群，这些集群可以通过考虑其基本的引文网络来进一步确定背景。该项目的具体目标包括探索对这种集群集合进行建模的设计空间，然后调查对它们进行视觉编码的方法，以便进行有效和有背景的比较。在以新的可视化技术的形式做出技术贡献的同时，我们还将制定指导方针、任务分类和系统的文献审查。应用程序示例以分析文档集合和生物组学数据为基础，为实际任务中的技术奠定基础，并允许与不同的应用程序专家合作。一个补充重点是促进对集群集合的协作视觉分析，例如，在启用多用户交互支持的墙壁大小的屏幕上显示。

Clustering automatically groups complex data items and creates abstractions based on data similarity. However, different criteria to compute entity similarity as well as different clustering algorithms are available, all producing a different grouping of the entities. The resulting variety of clustering ensembles is usually tried to be minimized, however, we see value and opportunities in the diversity. Only human experts can judge the quality of the results and choose among the multiple truths reflected in them. They can consider the specifics of the respective use case and assemble an overarching grouping based on various results. Collaboration of multiple experts might be commendable to embrace a broader set of expertise. To support human experts, we propose novel methods to visualize and visually compare such ensembles of clustering results. Our methods go beyond state-of-the-art approaches for visual comparison by addressing more complex scenarios regarding the characteristics of cluster structures, their contextual embedding in other data, and collaborative analysis. We want to investigate not only simple partitions of items, but overlapping groups and clusters embedded into contextual network structures. A specific focus are co-clusters, which also structure the data variables along the entities and thereby build groupings that are easier to understand. For instance, distinct topics can be identified from a set of scientific documents and, together with included terms, form co-clusters, which can be further contextualized by considering their underlying citation network. The specific objectives of the project include exploring the design space of modeling such ensembles of clusters, and then investigating means to visually encode them for effective and contextualized comparison. While making technical contributions in the form of new visualization techniques, we will also develop guidelines, task taxonomies, and systematic literature reviews. Application examples centered around the analysis of document collections and biological omics data ground the techniques in real-world tasks and allow working with different application experts. A complementing focus is facilitating the collaborative visual analysis of the clustering ensembles, for instance, displayed on a wall-sized screen enabled with multi-user interaction support.