On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study

2016/01/16 by Guilherme O. Campos, Arthur Zimek, Jörg Sander +5 · 2 citations
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #Imbalanced Data Classification Techniques #Water Systems and Optimization

paper · doi:10.1007/s10618-015-0444-8

crossref issued 2016/01/16 · crossref published 2016/01/16 · crossref published-online 2016/01/16 · openalex publication_date 2016/01/16 · crossref created 2016/01/16 · crossref published-print 2016/07/01 · crossref deposited 2019/09/03 · openalex created_date 2025/10/10 · crossref indexed 2026/08/01 · openalex updated_date 2026/08/01

Citations

• The Lack of A Priori Distinctions Between Learning Algorithms
• LOF
• The meaning and use of the area under a receiver operating characteristic (ROC) curve.
• The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
• The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance

Cited by

• There and back again: Outlier detection between statistical reasoning and data mining algorithms
• List of datasets for machine-learning research [wikipedia]