SOREX: Subspace Outlier Ranking Exploration Toolkit
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.
Cite
Text
Müller et al. "SOREX: Subspace Outlier Ranking Exploration Toolkit." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010. doi:10.1007/978-3-642-15939-8_44Markdown
[Müller et al. "SOREX: Subspace Outlier Ranking Exploration Toolkit." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2010.](https://mlanthology.org/ecmlpkdd/2010/muller2010ecmlpkdd-sorex/) doi:10.1007/978-3-642-15939-8_44BibTeX
@inproceedings{muller2010ecmlpkdd-sorex,
title = {{SOREX: Subspace Outlier Ranking Exploration Toolkit}},
author = {Müller, Emmanuel and Schiffer, Matthias and Gerwert, Patrick and Hannen, Matthias and Jansen, Timm and Seidl, Thomas},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2010},
pages = {607-610},
doi = {10.1007/978-3-642-15939-8_44},
url = {https://mlanthology.org/ecmlpkdd/2010/muller2010ecmlpkdd-sorex/}
}