The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Clustering Methods
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
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Tibshirani, R., Walther, G., & Hastie, T. (2001) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00293 · Cited by 6,205
Cited by
The Effects of Data Quality on Machine Learning Performance on Tabular Data
Mohammed, S., Budach, L., Feuerpfeil, M., Ihde, N., Nathansen, A., Noack, N., Patzlaff, H., Naumann, F., & Harmouch, H. (2025) · Information Systems · doi:10.1016/j.is.2025.102549 · Cited by 331
Hybrid approaches to optimization and machine learning methods: a systematic literature review
Azevedo, B. F., Rocha, A. M. A. C., & Pereira, A. I. (2024) · Machine Learning · doi:10.1007/s10994-023-06467-x · Cited by 291
A survey of Clustering Algorithms
Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245
Self-citationGLocalX—From Local to Global Explanations of Black Box AI Models
Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103457 · Cited by 140
Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition
Wang, J., Zhu, T., Chen, L. L., Ning, H., & Wan, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3239945 · Cited by 52
Metadata
- Authors
- Rokach, L., & Maimon, O.
- Venue
- Data Mining and Knowledge Discovery Handbook
- Publisher
- Springer-Verlag
- Published
- 2006-05-26
- Pages
- 321-352
- OpenAlex
- W4250042253
- Cited by
- 906
- References
- 3
- FWCI
- 11.43
- Type
- book chapter
- 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: Clustering Methods. https://app.cochat.ai/papers/10.1007/0-387-25465-x_15
- The paper
- Rokach, L., & Maimon, O. (2006). Clustering Methods. Data Mining and Knowledge Discovery Handbook. https://doi.org/10.1007/0-387-25465-x_15
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.
