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Koncar Philipp

Synthetic Dataset for Outlier Detection

Zenodo, 2018

This synthetically generated dataset can be used to evaluate outlier detection algorithms. It has 10 attributes and 1000 observations, of which 100 are labeled as outliers. Two-dimensional combinations of attributes form differently shaped clusters. Attribute 0 & Attribute 1: Two circular clusters Attribute 2 & Attribute 3: Two banana shaped clusters Attribute 4 & Attribute 5: Three point clouds Attribute 6 & Attribute 7: Two point clouds with variances Attribute 8 & Attribute 9: Three anisotropic shaped clusters. The "outlier" column states whether an observation is an outlier or not. Additionally, the .zip file contains 10 stratified randomized train test splits (70% train, 30% test).
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