title: A Five-Level Design Framework for Bicluster Visualizations.
abstract: Analysts often need to explore and identify coordinated relationships (e.g., four people who visited the same five cities on the same set of days) within some large datasets for sensemaking. Biclusters provide a potential solution to ease this process, because each computed bicluster bundles individual relationships into coordinated sets. By understanding such computed, structural, relations within biclusters, analysts can leverage their domain knowledge and intuition to determine the importance and relevance of the extracted relationships for making hypotheses. However, due to the lack of systematic design guidelines, it is still a challenge to design effective and usable visualizations of biclusters to enhance their perceptibility and interactivity for exploring coordinated relationships. In this paper, we present a five-level design framework for bicluster visualizations, with a survey of the state-of-the-art design considerations and applications that are related or that can be applied to bicluster visualizations. We summarize pros and cons of these design options to support user tasks at each of the five-level relationships. Finally, we discuss future research challenges for bicluster visualizations and their incorporation into visual analytics tools.
authors.forename: Maoyuan
authors.initials: M
authors.lastname: Sun
authors.forename: Chris
authors.initials: C
authors.lastname: North
authors.forename: Naren
authors.initials: N
authors.lastname: Ramakrishnan
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003196:Computer Graphics; D020869:Gene Expression Profiling; D006801:Humans; D048088:Informatics / Q000379:methods*; D008962:Models, Theoretical