Teaching talk:TUW - UE InfoVis WS 2010/11 - Gruppe 01 - Aufgabe 2: Difference between revisions
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Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise. | Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise. | ||
Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses. | Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses. | ||
Graphical inference tries to find a balance between these two methods: | Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions. | ||
The authors present two experimental protocols, Rorschach and Line-Up, which show how both | The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined. | ||
Revision as of 15:26, 16 November 2010
Euer Artikel:
Graphical Inference for Infovis Hadley Wickham, Dianne Cook, Heike Hofmann, Andreas Buja IEEE Transactions on Visualization and Computer Graphics (TVCG) November/December 2010 (vol. 16 no. 6):973-979, 2010.
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5613434 (Zugriff im TU Netzwerk)
-- Theresia Gschwandtner 07:42, 08 November 2010 (CEST)
Änderungsvorschläge
Introduction
da das Erkennen von nicht vorhandenen Mustern ja ausgeschlossen werden soll, würde ich es umformulieren:
Information visualization provides tools to show new relations in data. While this technique can be very effictive, it is threatened by apophenia, the human capability to detect patterns in random noise. Statistics instead provides methods that can examine if an assumption can be approved by the given data sample, and is therefore used to expose invalid hypotheses. Graphical inference tries to find a balance between these two methods: information visualisation to improve identification of new hypotheses, and statistics to reveal faulty conclusions.
The authors present two experimental protocols, Rorschach and Line-Up, which show how both techniques can be combined.
Additional Resources
Fun and Trivia
InfoVis1011 9925916 12:28, 16 November 2010 (CET)