<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Endert, A.</style></author><author><style face="normal" font="default" size="100%">Chao Han</style></author><author><style face="normal" font="default" size="100%">Maiti, D.</style></author><author><style face="normal" font="default" size="100%">House, L.</style></author><author><style face="normal" font="default" size="100%">Leman, S.</style></author><author><style face="normal" font="default" size="100%">North, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Observation-level interaction with statistical models for visual analytics</style></title><secondary-title><style face="normal" font="default" size="100%">Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">data analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">data interactive visual exploration</style></keyword><keyword><style  face="normal" font="default" size="100%">data visualisation</style></keyword><keyword><style  face="normal" font="default" size="100%">exploratory interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">expressive interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">generative topographic mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">multidimensional scaling</style></keyword><keyword><style  face="normal" font="default" size="100%">observation-level interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">parameter adjustments</style></keyword><keyword><style  face="normal" font="default" size="100%">principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">probabilistic principal component analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">probability</style></keyword><keyword><style  face="normal" font="default" size="100%">sensemaking process</style></keyword><keyword><style  face="normal" font="default" size="100%">statistical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Analytics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">oct.</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">121 -130</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record></records></xml>