Visual Analytics with Biclusters
Identifying coordinated relationships is an important task in data analytics. For example, an intelligence analyst might want to find three suspicious people who 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.
We propose to develop a visual analytics approach to assist exploring coordinated relationships with biclusters, because each computed bicluster algorithmically 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 is challenging. To address this, this work highlights three key contributions: 1) a five-level design framework for bicluster visualizations, 2) an edge oriented design concept that displays biclusters as edge bundles in context between entity visualizations (e.g., entity lists), and 3) evaluating the potential role of visualized biclusters to support the exploration of coordinated relationships.