The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
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
Cited by
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-citationA 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
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
Statistical selection of relevant subspace projections for outlier ranking
Muller, E., Schiffer, M., & Seidl, T. (2011) · 2011 IEEE 27th International Conference on Data Engineering · doi:10.1109/icde.2011.5767916 · Cited by 126
Metadata
- Authors
- Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2009
- Pages
- 831-838
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W199035648
- Cited by
- 314
- References
- 3
- FWCI
- 6.11
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. https://app.cochat.ai/papers/10.1007/978-3-642-01307-2_86
- The paper
- Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009). Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-01307-2_86
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.