SOREX: subspace outlier ranking exploration toolkit

Authors:
Emmanuel Müller;Matthias Schiffer;Patrick Gerwert;Matthias Hannen;Timm Jansen;Thomas Seidl
Affiliations:
Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany;Data Management and Data Exploration Group, RWTH Aachen University, Germany
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Year:
2010

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Abstract

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.