The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Integrating constraints and metric learning in semi-supervised clustering
Abstract
Semi-supervised clustering employs a small amount of labeled data to aid unsupervised learning. Previous work in the area has utilized supervised data in one of two approaches: 1) constraint-based methods that guide the clustering algorithm towards a better grouping of the data, and 2) distance-function learning methods that adapt the underlying similarity metric used by the clustering algorithm. This paper provides new methods for the two approaches as well as presents a new semi-supervised clustering algorithm that integrates both of these techniques in a uniform, principled framework. Experimental results demonstrate that the unified approach produces better clusters than both individual approaches as well as previously proposed semi-supervised clustering algorithms.
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
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Distance Metric Learning with Application to Clustering with Side-Information
Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2002) · Cited by 2,700
Constrained K-means Clustering with Background Knowledge
Wagstaff, K. L., Cardie, C., Rogers, S., & Schrödl, S. (2001) · Cited by 2,469
Bilmes, J. A. (1998) · Cited by 2,451
Adaptive duplicate detection using learnable string similarity measures
Bilenko, M., & Mooney, R. J. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956759 · Cited by 926
Self-citationA probabilistic framework for semi-supervised clustering
Basu, S., Bilenko, M., & Mooney, R. J. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014062 · Cited by 814
Self-citationSemi-supervised Clustering by Seeding
Basu, S., Banerjee, A., & Mooney, R. J. (2002) · Cited by 803
Self-citationLearning a Distance Metric from Relative Comparisons
Schultz, M., & Joachims, T. (2003) · Cited by 609
Learning distance functions using equivalence relations
Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003) · Cited by 433
Discovering molecular pathways from protein interaction and gene expression data
Segal, E., Wang, H., & Koller, D. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1037 · Cited by 302
Cited by
(2006) · The MIT Press eBooks · doi:10.7551/mitpress/9780262033589.001.0001 · Cited by 4,280
A survey of content-based image retrieval with high-level semantics
Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.045 · Cited by 1,696
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
Learning a Mahalanobis distance metric for data clustering and classification
Xiang, S., Nie, F., & Zhang, C. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.05.018 · Cited by 621
A Survey on Metric Learning for Feature Vectors and Structured Data
Bellet, A., Habrard, A., & Sebban, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.6709 · Cited by 494
Metadata
- Authors
- Bilenko, M., Basu, S., & Mooney, R. J.
- Venue
- Twenty-first international conference on Machine learning - ICML '04
- Publisher
- ACM Press
- Published
- 2004
- Pages
- 11
- OpenAlex
- W2096100960
- Cited by
- 849
- References
- 13
- Type
- conference abstract
- 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: Integrating constraints and metric learning in semi-supervised clustering. https://app.cochat.ai/papers/10.1145/1015330.1015360
- The paper
- Bilenko, M., Basu, S., & Mooney, R. J. (2004). Integrating constraints and metric learning in semi-supervised clustering. Twenty-first international conference on Machine learning - ICML '04. https://doi.org/10.1145/1015330.1015360
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.
