@article {6875974,
	title = {A Five-Level Design Framework for Bicluster Visualizations},
	journal = {Visualization and Computer Graphics, IEEE Transactions on},
	volume = {20},
	number = {12},
	year = {2014},
	month = {Dec},
	pages = {1713-1722},
	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.},
	keywords = {bicluster visualizations, Biclusters, Bioinformatics, Cluster approximation, coordinated relationships, data analysis, Data mining, data visualisation, design framework, five-level design framework, interactive visual analytics, navigation, pattern clustering, Visual Analytics, visual analytics tools},
	issn = {1077-2626},
	doi = {10.1109/TVCG.2014.2346665},
	author = {Sun, Maoyuan and North, C. and Ramakrishnan, N.}
}
@conference {6102495,
	title = {Analyst{\textquoteright}s workspace: Protecting vastopolis},
	booktitle = {Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on},
	year = {2011},
	month = {Oct},
	pages = {323-324},
	abstract = {Analyst{\textquoteright}s Workspace is a sensemaking environment designed specifically for use of large, high-resolution displays. It employs a spatial workspace to integrate foraging and synthesis activities into a unified process. In this paper we describe how Analyst{\textquoteright}s Workspace solved the VAST 2011 mini-challenge $\#$3 and discuss some of the unique features of the environment.
},
	keywords = {analyst workspace, Bioterrorism, Browsers, computer displays, data analysis, data visualisation, high-resolution displays, Intelligence Analysis, large, Marine animals, Rivers, sensemaking environment, space, VAST 2011 mini-challenge $\#$3, Vastopolis protection, Visual Analytics},
	doi = {10.1109/VAST.2011.6102495},
	author = {Andrews, C. and Hossain, M.S. and Gad, S. and Ramakrishnan, N. and North, C.}
}
