On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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...
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| Published in | Data mining and knowledge discovery Vol. 30; no. 4; pp. 891 - 927 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.07.2016
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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 benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard
k
nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results. |
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| AbstractList | 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 benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results. 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 benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results. |
| Author | Schubert, Erich Sander, Jörg Campello, Ricardo J. G. B. Assent, Ira Houle, Michael E. Campos, Guilherme O. Zimek, Arthur Micenková, Barbora |
| Author_xml | – sequence: 1 givenname: Guilherme O. surname: Campos fullname: Campos, Guilherme O. organization: University of São Paulo – sequence: 2 givenname: Arthur surname: Zimek fullname: Zimek, Arthur email: zimek@dbs.ifi.lmu.de organization: Ludwig-Maximilians-Universität München – sequence: 3 givenname: Jörg surname: Sander fullname: Sander, Jörg organization: Department of Computing Science, University of Alberta – sequence: 4 givenname: Ricardo J. G. B. surname: Campello fullname: Campello, Ricardo J. G. B. organization: University of São Paulo – sequence: 5 givenname: Barbora surname: Micenková fullname: Micenková, Barbora organization: Department of Computer Science, Aarhus University – sequence: 6 givenname: Erich surname: Schubert fullname: Schubert, Erich organization: Ludwig-Maximilians-Universität München – sequence: 7 givenname: Ira surname: Assent fullname: Assent, Ira organization: Department of Computer Science, Aarhus University – sequence: 8 givenname: Michael E. surname: Houle fullname: Houle, Michael E. organization: National Institute of Informatics |
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| ContentType | Journal Article |
| Copyright | The Author(s) 2016 |
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| References_xml | – reference: Estivill-CastroVWhy so many clustering algorithms—a position paperACM SIGKDD Explor2002416575119252510.1145/568574.568575 – reference: Bache K, Lichman M (2013) UCI machine learning repository. http://archive.ics.uci.edu/ml – reference: de VriesTChawlaSHouleMEDensity-preserving projections for large-scale local anomaly detectionKnowl Inf Syst2012321255210.1007/s10115-011-0430-4 – reference: Craswell N (2009b) R-precision. In: Liu L, Özsu MT (eds) Encyclopedia of database systems. Springer, Berlin, p 2453. doi:10.1007/978-0-387-39940-9_486 – reference: Schubert E, Zimek A, Kriegel HP (2015b) Fast and scalable outlier detection with approximate nearest neighbor ensembles. In: Proceedings of the 20th international conference on database systems for advanced applications (DASFAA), Hanoi, Vietnam, pp 19–36. doi:10.1007/978-3-319-18123-3_2 – reference: Nguyen HV, Ang HH, Gopalkrishnan V (2010) Mining outliers with ensemble of heterogeneous detectors on random subspaces. In: Proceedings of the 15th international conference on database systems for advanced applications (DASFAA), Tsukuba, pp 368–383. doi:10.1007/978-3-642-12026-8_29 – reference: de Vries T, Chawla S, Houle ME (2010) Finding local anomalies in very high dimensional space. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), Sydney, pp 128–137. doi:10.1109/ICDM.2010.151 – reference: Kriegel HP, Schubert E, Zimek A (2015) The (black) art of runtime evaluation: Are we comparing algorithms or implementations? submitted – reference: HanleyJAMcNeilBJThe meaning and use of the area under a receiver operating characteristic (ROC) curveRadiology1982143293610.1148/radiology.143.1.7063747 – reference: Schubert E, Wojdanowski R, Zimek A, Kriegel HP (2012) On evaluation of outlier rankings and outlier scores. In: Proceedings of the 12th SIAM international conference on data mining (SDM), Anaheim, pp 1047–1058. doi:10.1137/1.9781611972825.90 – reference: Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd international conference on machine learning (ICML), Pittsburgh, pp 233–240 – reference: DemšarJStatistical comparisons of classifiers over multiple data setsJ Mach Learn Res2006713022743601222.68184 – reference: Dang XH, Assent I, Ng RT, Zimek A, Schubert E (2014) Discriminative features for identifying and interpreting outliers. In: Proceedings of the 30th International Conference on Data Engineering (ICDE), Chicago, pp 88–99. doi:10.1109/ICDE.2014.6816642 – reference: HubertLArabiePComparing partitionsJ Classif19852119321810.1007/BF019080750587.62128 – reference: ZimmermannAThe data problem in data miningACM SIGKDD Explor2014162384510.1145/2783702.2783706 – reference: VendraminLCampelloRJGBHruschkaERRelative clustering validity criteria: a comparative overviewStat Anal Data Min2010342092352672774 – reference: AngiulliFPizzutiCOutlier mining in large high-dimensional data setsIEEE Trans Knowl Data Eng2005172203215223153610.1109/TKDE.2005.311084.68140 – reference: Müller E, Assent I, Iglesias P, Mülle Y, Böhm K (2012) Outlier ranking via subspace analysis in multiple views of the data. 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| Title | On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study |
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