This repository, devised mainly for the evaluation of unsupervised outlier detection, contains: 1) a compiled collection of over twenty real-world basis datasets as well as hundreds of variants of these datasets pre-processed for outlier detection according to different procedures (e.g., subsampling and normalization); 2) the whole set of raw results obtained by applying on these datasets 12 well established outlier detection methods, within a range of different parameter values; and 3) aggregated results, statistics, and visualizations over all the methods and datasets.

CLICK HERE to access the repository.

NOTE: please, refer to our related paper when referencing this repository:

G.O. Campos, A. Zimek, J. Sander, R. J.G.B. Campello, B. Micenková, E. Schubert, I. Assent and M. E. Houle “On the Evaluation of Unsupervised Outlier Detection: Measures, Datasets, and an Empirical Study”, Data Mining and Knowledge Discovery (DOI 10.1007/s10618-015-0444-8).