The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The (black) art of runtime evaluation: Are we comparing algorithms or implementations?
Overview
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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.
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Citations
Cites
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
(2007) · doi:10.5555/1283383.1283494 · Cited by 7,364
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
An efficient k-means clustering algorithm: analysis and implementation
Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017616 · Cited by 5,660
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-citation
Cited by
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017) · ACM Transactions on Database Systems · doi:10.1145/3068335 · Cited by 2,737
Self-citationdbscan: Fast Density-Based Clustering with R
Hahsler, M., Piekenbrock, M., & Doran, D. (2019) · Journal of Statistical Software · doi:10.18637/jss.v091.i01 · Cited by 802
Frequent itemset mining: A 25 years review
Luna, J. M., Fournier‐Viger, P., & Ventura, S. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1329 · Cited by 258
Balamuralidhar, N., Tilon, S., & Nex, F. (2021) · Remote Sensing · doi:10.3390/rs13040573 · Cited by 104
Metadata
- Authors
- Kriegel, H.-P., Schubert, E., & Zimek, A.
- Venue
- Knowledge and Information Systems
- Published
- 2016-10-22
- Volume
- 52
- Issue
- 2
- Pages
- 341-378
- ISSN
- 0219-1377 (ISSN-L)
- 0219-3116
- OpenAlex
- W2532189199
- Cited by
- 229
- References
- 6
- FWCI
- 40.20
- Type
- journal article
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The (black) art of runtime evaluation: Are we comparing algorithms or implementations? https://app.cochat.ai/papers/10.1007/s10115-016-1004-2
- The paper
- Kriegel, H.-P., Schubert, E., & Zimek, A. (2017). The (black) art of runtime evaluation: Are we comparing algorithms or implementations? Knowledge and Information Systems. https://doi.org/10.1007/s10115-016-1004-2
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.
