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ECHT '92 Proceedings of the ACM conference on Hypertext
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Visualizing the non-visual: spatial analysis and interaction with information for text documents
Readings in information visualization
Computer Supported Cooperative Work
A Scalable Generative Topographic Mapping for Sparse Data Sequences
ITCC '05 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume I - Volume 01
Toward a Deeper Understanding of the Role of Interaction in Information Visualization
IEEE Transactions on Visualization and Computer Graphics
WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Recovering Reasoning Processes from User Interactions
IEEE Computer Graphics and Applications
Clustering and exploring search results using timeline constructions
Proceedings of the 18th ACM conference on Information and knowledge management
Space to think: large high-resolution displays for sensemaking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Information Visualization
Semantic interaction for visual text analytics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A cartographic approach to visualizing conference abstracts
IEEE Computer Graphics and Applications
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Information visualization on large, high-resolution displays: issues, challenges, and opportunities
Information Visualization - Special issue on State of the Field and New Research Directions
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Analyzing complex textual datasets consists of identifying connections and relationships within the data based on users' intuition and domain expertise. In a spatial workspace, users can do so implicitly by spatially arranging documents into clusters to convey similarity or relationships. Algorithms exist that spatialize and cluster such information mathematically based on similarity metrics. However, analysts often find inconsistencies in these generated clusters based on their expertise. Therefore, to support sensemaking, layouts must be co-created by the user and the model. In this paper, we present the results of a study observing individual users performing a sensemaking task in a spatial workspace. We examine the users' interactions during their analytic process, and also the clusters the users manually created. We found that specific interactions can act as valuable indicators of important structure within a dataset. Further, we analyze and characterize the structure of the user-generated clusters to identify useful metrics to guide future algorithms. Through a deeper understanding of how users spatially cluster information, we can inform the design of interactive algorithms to generate more meaningful spatializations for text analysis tasks, to better respond to user interactions during the analytics process, and ultimately to allow analysts to more rapidly gain insight.