TY - CONF T1 - Observation-level interaction with statistical models for visual analytics T2 - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on Y1 - 2011 A1 - Endert, Alex A1 - Chao Han A1 - Maiti, Dipayan A1 - House, Leanna A1 - Leman, Scotland A1 - North, Chris KW - data analysis KW - data interactive visual exploration KW - data visualisation KW - exploratory interaction KW - expressive interaction KW - generative topographic mapping KW - multidimensional scaling KW - observation-level interaction KW - parameter adjustments KW - principal component analysis KW - probabilistic principal component analysis KW - probability KW - sensemaking process KW - statistical models KW - Visual Analytics AB - In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus #x201C;observation #x201D;) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools. JF - Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on ER -