SOREX: Subspace Outlier Ranking Exploration Toolkit

Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. In recent research novel outlier ranking methods propose to focus on outliers hidden in subspace projections of the data. However, focusing only on the detection of outliers these approaches miss to provide reasons why an object should be considered as an outlier.

 

In this work, we propose a novel toolkit for exploration of subspace outlier rankings. To enable exploration of subspace outliers and to complete knowledge extraction we provide further descriptive information in addition to the pure detection of outliers. As wittinesses for the outlierness of an object, we provide information about the relevant projections describing the reasons for outlier properties. We provided SOREX as open source framework on our website it is easily extensible and suitable for research and educational purposes in this emerging research area.

Authors: Müller E., Schiffer M., Gerwert P., Hannen M., Jansen T., Seidl T.
Published in: Proc. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), Barcelona, Spain, Springer, LNAI 6323
Publisher: Springer - Heidelberg, Germany
Language: EN
Year: 2010
Additional:

(Demo)

Pages: 607-610
ISBN: 978-3-642-15938-1
Conference: ECML PKDD
DOI:10.1007/978-3-642-15939-8_44
Url:ECML PKDD 2010
Type: Conference papers (peer reviewed)
Research topic: Data Analysis and Knowledge Extraction