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

聚类自动对复杂的数据项进行分组，并根据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。由此产生的聚类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，并在其中反映的多个真理中做出选择。他们可以考虑各个用例的细节，并根据各种结果组装一个总体分组。多名专家的合作可能值得赞扬，以涵盖更广泛的专门知识。为了支持人类专家，我们提出了新的方法来可视化和可视化地比较聚类结果的集合。我们的方法超越了最先进的视觉比较方法，解决了更复杂的情况下，集群结构的特点，他们的上下文嵌入在其他数据和协作分析。我们不仅要研究项目的简单划分，还要研究嵌入到上下文网络结构中的重叠组和聚类。一个特定的焦点是协同聚类，它也将数据变量沿着实体进行结构化，从而构建更容易理解的分组。例如，可以从一组科学文献中识别不同的主题，并与所包含的术语一起形成共聚类，可以通过考虑其潜在的引用网络来进一步将其置于上下文中。该项目的具体目标包括探索设计空间的集群建模这样的合奏，然后调查的手段，以视觉编码有效的和上下文的比较。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综述。围绕文档集合和生物组学数据分析的应用示例将这些技术应用于现实任务，并允许与不同的应用专家合作。一个补充的重点是促进聚类集合的协作视觉分析，例如，显示在一个墙壁大小的屏幕上，支持多用户交互。

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.