On exploring complex relationships of correlation clusters

Elke Achtert*, Christian Böhm, Hans Peter Kriegel, Peer Kröger, Arthur Zimek

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. In addition, these so-called correlation clusters may have complex relationships between each other. For example, a correlation cluster in a 1-D subspace (forming a line) may be enclosed within one or even several correlation clusters in 2D superspaces (forming planes). In general, such relationships can be seen as a complex hierarchy that allows multiple inclusions, i.e. clusters may be embedded in several super-clusters rather than only in one. Obviously, uncovering the hierarchical relationships between the detected correlation clusters is an important information gain. Since existing approaches cannot detect such complex hierarchical relationships among correlation clusters, we propose the algorithm ERiC to tackle this problem and to visualize the result by means of a graph-based representation. In our experimental evaluation, we show that ERiC finds more information than state-of-the-art correlation clustering methods and outperforms existing competitors in terms of efficiency.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
Number of pages10
PublisherIEEE
Publication date23. Jul 2007
ISBN (Electronic)0-7695-2868-6
DOIs
Publication statusPublished - 23. Jul 2007
Externally publishedYes
Event19th International Conference on Scientific and Statistical Database Management - Banff, Canada
Duration: 9. Jul 200711. Jul 2007

Conference

Conference19th International Conference on Scientific and Statistical Database Management
CountryCanada
CityBanff
Period09/07/200711/07/2007
SponsoriCORE - Alberta's Informatics Circle of Research Excellence, University of Calgary, University of Calgary

Cite this

Achtert, E., Böhm, C., Kriegel, H. P., Kröger, P., & Zimek, A. (2007). On exploring complex relationships of correlation clusters. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007 IEEE. https://doi.org/10.1109/SSDBM.2007.21
Achtert, Elke ; Böhm, Christian ; Kriegel, Hans Peter ; Kröger, Peer ; Zimek, Arthur. / On exploring complex relationships of correlation clusters. Proceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007. IEEE, 2007.
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abstract = "In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. In addition, these so-called correlation clusters may have complex relationships between each other. For example, a correlation cluster in a 1-D subspace (forming a line) may be enclosed within one or even several correlation clusters in 2D superspaces (forming planes). In general, such relationships can be seen as a complex hierarchy that allows multiple inclusions, i.e. clusters may be embedded in several super-clusters rather than only in one. Obviously, uncovering the hierarchical relationships between the detected correlation clusters is an important information gain. Since existing approaches cannot detect such complex hierarchical relationships among correlation clusters, we propose the algorithm ERiC to tackle this problem and to visualize the result by means of a graph-based representation. In our experimental evaluation, we show that ERiC finds more information than state-of-the-art correlation clustering methods and outperforms existing competitors in terms of efficiency.",
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Achtert, E, Böhm, C, Kriegel, HP, Kröger, P & Zimek, A 2007, On exploring complex relationships of correlation clusters. in Proceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007. IEEE, 19th International Conference on Scientific and Statistical Database Management, Banff, Canada, 09/07/2007. https://doi.org/10.1109/SSDBM.2007.21

On exploring complex relationships of correlation clusters. / Achtert, Elke; Böhm, Christian; Kriegel, Hans Peter; Kröger, Peer; Zimek, Arthur.

Proceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007. IEEE, 2007.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

TY - GEN

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AU - Böhm, Christian

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AU - Kröger, Peer

AU - Zimek, Arthur

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N2 - In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. In addition, these so-called correlation clusters may have complex relationships between each other. For example, a correlation cluster in a 1-D subspace (forming a line) may be enclosed within one or even several correlation clusters in 2D superspaces (forming planes). In general, such relationships can be seen as a complex hierarchy that allows multiple inclusions, i.e. clusters may be embedded in several super-clusters rather than only in one. Obviously, uncovering the hierarchical relationships between the detected correlation clusters is an important information gain. Since existing approaches cannot detect such complex hierarchical relationships among correlation clusters, we propose the algorithm ERiC to tackle this problem and to visualize the result by means of a graph-based representation. In our experimental evaluation, we show that ERiC finds more information than state-of-the-art correlation clustering methods and outperforms existing competitors in terms of efficiency.

AB - In high dimensional data, clusters often only exist in arbitrarily oriented subspaces of the feature space. In addition, these so-called correlation clusters may have complex relationships between each other. For example, a correlation cluster in a 1-D subspace (forming a line) may be enclosed within one or even several correlation clusters in 2D superspaces (forming planes). In general, such relationships can be seen as a complex hierarchy that allows multiple inclusions, i.e. clusters may be embedded in several super-clusters rather than only in one. Obviously, uncovering the hierarchical relationships between the detected correlation clusters is an important information gain. Since existing approaches cannot detect such complex hierarchical relationships among correlation clusters, we propose the algorithm ERiC to tackle this problem and to visualize the result by means of a graph-based representation. In our experimental evaluation, we show that ERiC finds more information than state-of-the-art correlation clustering methods and outperforms existing competitors in terms of efficiency.

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Achtert E, Böhm C, Kriegel HP, Kröger P, Zimek A. On exploring complex relationships of correlation clusters. In Proceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007. IEEE. 2007 https://doi.org/10.1109/SSDBM.2007.21