Visualizing the evolution of Web ecologies
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive visualization of serial periodic data
Proceedings of the 11th annual ACM symposium on User interface software and technology
ACM Computing Surveys (CSUR)
Recursive Pattern: A Technique for Visualizing Very Large Amounts of Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Cluster and Calendar Based Visualization of Time Series Data
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Visualizing Time-Series on Spirals
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Arc Diagrams: Visualizing Structure in Strings
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Proceedings of the 35th conference on Winter simulation: driving innovation
Rearrangement Clustering: Pitfalls, Remedies, and Applications
The Journal of Machine Learning Research
Visualisation interactive de données temporelles: un aperçu de l'état de l'art
Conference Internationale Francophone sur I'Interaction Homme-Machine
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We 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.