TY - CONF T1 - SIRIUS: Dual, Symmetric, Interactive Dimension Reductions T2 - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2018 A1 - Michelle Dowling A1 - Wenskovitch, John A1 - J.T. Fry A1 - Leman, Scotland A1 - House, Leanna A1 - North, Chris KW - attribute projection KW - dimension reduction KW - exploratory data analysis KW - observation projection KW - Semantic interaction AB - Much research has been done regarding how to visualize and interact with observations and attributes of high-dimensional data for exploratory data analysis. From the analyst's perceptual and cognitive perspective, current visualization approaches typically treat the observations of the high-dimensional dataset very differently from the attributes. Often, the attributes are treated as inputs (e.g., sliders), and observations as outputs (e.g., projection plots), thus emphasizing investigation of the observations. However, there are many cases in which analysts wish to investigate both the observations and the attributes of the dataset, suggesting a symmetry between how analysts think about attributes and observations. To address this, we define SIRIUS (Symmetric Interactive Representations In a Unified System), a symmetric, dual projection technique to support exploratory data analysis of high-dimensional data. We provide an example implementation of SIRIUS and demonstrate how this symmetry affords additional insights. JF - 2018 IEEE Conference on Visual Analytics Science and Technology (VAST) ER - TY - CONF T1 - StarSpire: Multi-Model Semantic Interaction for Text Analytics T2 - IEEE Conference on Visual Analytics Science and Technology (VAST) Y1 - 2014 A1 - Lauren Bradel A1 - North, Chris A1 - House, Leanna A1 - Leman, Scotland AB - Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection. JF - IEEE Conference on Visual Analytics Science and Technology (VAST) PB - IEEE CY - Paris, France ER - TY - JOUR T1 - Semantics of Directly Manipulating Spatializations JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2013 A1 - Hu, Xinran A1 - Lauren Bradel A1 - Maiti, Dipayan A1 - House, Leanna A1 - North, Chris A1 - Leman, Scotland VL - 19 IS - 12 JO - IEEE Trans. Visual. Comput. Graphics ER - TY - CONF T1 - The semantics of clustering: analysis of user-generated spatializations of text documents T2 - Proceedings of the International Working Conference on Advanced Visual Interfaces Y1 - 2012 A1 - Endert, Alex A1 - Fox, Seth A1 - Maiti, Dipayan A1 - Leman, Scotland A1 - North, Chris KW - clustering KW - text analytics KW - Visual Analytics KW - visualization AB - 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. JF - Proceedings of the International Working Conference on Advanced Visual Interfaces T3 - AVI '12 PB - ACM CY - New York, NY, USA SN - 978-1-4503-1287-5 UR - http://doi.acm.org/10.1145/2254556.2254660 ER -