@conference {6102449,
	title = {Observation-level interaction with statistical models for visual analytics},
	booktitle = {Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on},
	year = {2011},
	month = {oct.},
	pages = {121 -130},
	abstract = {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.},
	keywords = {data analysis, data interactive visual exploration, data visualisation, exploratory interaction, expressive interaction, generative topographic mapping, multidimensional scaling, observation-level interaction, parameter adjustments, principal component analysis, probabilistic principal component analysis, probability, sensemaking process, statistical models, Visual Analytics},
	doi = {10.1109/VAST.2011.6102449},
	author = {Endert, Alex and Chao Han and Maiti, Dipayan and House, Leanna and Leman, Scotland and North, Chris}
}
