Extension de DataTube pour la fouille visuelle de données temporelles

International audienceWe deal in this paper with visual mining of temporal data, where data are represented by n time-dependent attributes (or series). We describe the state of the art in temporal data visualization, and we concentrate on a specific visualization (DataTube) which has received yet little attention. DataTube uses a tubular shape to represent the data. The axis of the tube represents the time. We perform several extensions to this visualization: we define several visualizations (color, shapes, etc) and we add a temporal axis. We introduce several interactions with the possibility to select attributes and time steps, or to add annotation on the visualization. We add a clustering algorithm in order to cluster together the attributes with similar behavior. We integrate this visualization in our data mining virtual reality platform VRMiner (with stereoscopic display and interactive navigation). We apply this visualization to several real-world data sets and we show that it can deal with 1, 5 million values