On exploring complex relationships of correlation clusters

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

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Abstrakt

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.

OriginalsprogEngelsk
TitelProceedings of the 19th International Conference on Scientific and Statistical Database Management, SSDBM 2007
Antal sider10
ForlagIEEE
Publikationsdato23. jul. 2007
ISBN (Elektronisk)0-7695-2868-6
DOI
StatusUdgivet - 23. jul. 2007
Udgivet eksterntJa
Begivenhed19th International Conference on Scientific and Statistical Database Management - Banff, Canada
Varighed: 9. jul. 200711. jul. 2007

Konference

Konference19th International Conference on Scientific and Statistical Database Management
LandCanada
ByBanff
Periode09/07/200711/07/2007
SponsoriCORE - Alberta's Informatics Circle of Research Excellence, University of Calgary, University of Calgary

Citationsformater

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