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

• Published in: Data mining and knowledge discovery Vol. 30; no. 4; pp. 891 - 927
• Main Authors: Campos, Guilherme O., Zimek, Arthur, Sander, Jörg, Campello, Ricardo J. G. B., Micenková, Barbora, Schubert, Erich, Assent, Ira, Houle, Michael E.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2016; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Benchmarking; Chemistry and Earth Sciences; Computer Science; Constants; Data analysis; Data mining; Data Mining and Knowledge Discovery; Datasets; Information Storage and Retrieval; Outliers (statistics); Physics; Semantics; Statistics for Engineering; Tasks; Datasets; Evaluation; Unsupervised outlier detection; Measures

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate be...