The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
Abstract
Ensembles for unsupervised outlier detection is an emerging topic that has been neglected for a surprisingly long time (although there are reasons why this is more difficult than supervised ensembles or even clustering ensembles). Aggarwal recently discussed algorithmic patterns of outlier detection ensembles, identified traces of the idea in the literature, and remarked on potential as well as unlikely avenues for future transfer of concepts from supervised ensembles. Complementary to his points, here we focus on the core ingredients for building an outlier ensemble, discuss the first steps taken in the literature, and identify challenges for future research.
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Cites
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408
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Finding Groups in Data: An Introduction to Cluster Analysis
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LOF: identifying density-based local outliers
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Self-citationLOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Self-citationHawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
Rokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy
Kuncheva, L. I., & Whitaker, C. J. (2003) · Machine Learning · doi:10.1023/a:1022859003006 · Cited by 2,335
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864
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Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
Goldstein, M., & Uchida, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0152173 · Cited by 1,032
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
Self-citationProgress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
A comparative evaluation of outlier detection algorithms: Experiments and analyses
Domingues, R., Filippone, M., Michiardi, P., & Zouaoui, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.09.037 · Cited by 452
Metadata
- Authors
- Zimek, A., Campello, R. J. G. B., & Sander, J.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2014-03-17
- Volume
- 15
- Issue
- 1
- Pages
- 11-22
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2101549186
- Cited by
- 278
- References
- 9
- FWCI
- 21.46
- Type
- journal article
- Language
- en
- Open access
- closed
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