Visual Analytics with Biclusters: Exploring Coordinated Relationships in Context
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Exploring coordinated relationships is an important task in data analytics. For example, an intelligence analyst may want to find three suspicious people who all visited the same four cities. However, existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. This work presents a visual analytics approach that applies biclusters to support coordinated relationships exploration. Each computed bicluster aggregates individual relationships into coordinated sets. Thus, coordinated relationships can be formalized as biclusters. However, how to incorporate biclusters into a visual analytics tool to support sensemaking tasks is challenging. To address this, this work features three key contributions: 1) a five-level design framework for bicluster visualizations, 2) BiSet, highlighting bicluster-based edge bundling, seriation-based multiple lists ordering, and interactions for dynamic information foraging and management, and 3) an evaluation of BiSet.
- Doctoral Dissertations