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
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Paulheim, Heiko; Meusel, Robert: A decomposition of the outlier detection problem into a set of supervised learning problems (2015)
- Schubert, Erich; Zimek, Arthur; Kriegel, Hans-Peter: Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection (2014)
- Müller, Emmanuel; Schiffer, Matthias; Gerwert, Patrick; Hannen, Matthias; Jansen, Timm; Seidl, Thomas: SOREX: subspace outlier ranking exploration toolkit (2010)