KOPS - Das Institutionelle Repositorium der Universität Konstanz

Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data

Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

TATU, Andrada, Fabian MAASS, Ines FÄRBER, Enrico BERTINI, Tobias SCHRECK, Thomas SEIDL, Daniel KEIM, 2012. Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data. 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). Seattle, WA, USA, 14. Okt 2012 - 19. Okt 2012. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). 2012 IEEE Conference on Visual Analytics Science and Technology (VAST). Seattle, WA, USA, 14. Okt 2012 - 19. Okt 2012. IEEE, pp. 63-72. ISBN 978-1-4673-4752-5. Available under: doi: 10.1109/VAST.2012.6400488

@inproceedings{Tatu2012-10Subsp-22543, title={Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data}, year={2012}, doi={10.1109/VAST.2012.6400488}, isbn={978-1-4673-4752-5}, publisher={IEEE}, booktitle={2012 IEEE Conference on Visual Analytics Science and Technology (VAST)}, pages={63--72}, author={Tatu, Andrada and Maaß, Fabian and Färber, Ines and Bertini, Enrico and Schreck, Tobias and Seidl, Thomas and Keim, Daniel} }

Maaß, Fabian Schreck, Tobias Keim, Daniel Maaß, Fabian deposit-license Bertini, Enrico Schreck, Tobias 2012-10 Subspace Search and Visualization to Make Sense of Alternative Clusterings in High-Dimensional Data 2013-05-08T12:06:07Z Färber, Ines Bertini, Enrico Tatu, Andrada Seidl, Thomas Färber, Ines In explorative data analysis, the data under consideration often resides in a high-dimensional (HD) data space. Currently many methods are available to analyze this type of data. So far, proposed automatic approaches include dimensionality reduction and cluster analysis, whereby visual-interactive methods aim to provide effective visual mappings to show, relate, and navigate HD data. Furthermore, almost all of these methods conduct the analysis from a singular perspective, meaning that they consider the data in either the original HD data space, or a reduced version thereof. Additionally, HD data spaces often consist of combined features that measure different properties, in which case the particular relationships between the various properties may not be clear to the analysts a priori since it can only be revealed if appropriate feature combinations (subspaces) of the data are taken into consideration. Considering just a single subspace is, however, often not sufficient since different subspaces may show complementary, conjointly, or contradicting relations between data items. Useful information may consequently remain embedded in sets of subspaces of a given HD input data space. Relying on the notion of subspaces, we propose a novel method for the visual analysis of HD data in which we employ an interestingness-guided subspace search algorithm to detect a candidate set of subspaces. Based on appropriately defined subspace similarity functions, we visualize the subspaces and provide navigation facilities to interactively explore large sets of subspaces. Our approach allows users to effectively compare and relate subspaces with respect to involved dimensions and clusters of objects. We apply our approach to synthetic and real data sets. We thereby demonstrate its support for understanding HD data from different perspectives, effectively yielding a more complete view on HD data. 2013-05-08T12:06:07Z Tatu, Andrada Keim, Daniel IEEE Conference on Visual Analytics Science & Technology 2012 : Seattle, Washington, USA, 14 - 19 October 2012 ; Proceedings / Giuseppe Santucci and Matthew Ward (eds.). - Piscataway, NJ : IEEE, 2012, S. 63-72. - ISBN 978-1-4673-4753-2 Seidl, Thomas eng

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto