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Agglomerative clustering using the concept of mutual nearest neighbourhood
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Citations
Cited by
Joint Unsupervised Learning of Deep Representations and Image Clusters
Yang, J., Parikh, D., & Batra, D. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.556 · Cited by 879
Li, Y., Hu, P., Liu, Z., Peng, D., Zhou, J. T., & Peng, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17037 · Cited by 714
Analyzing Encoded Concepts in Transformer Language Models
Sajjad, H., Durrani, N., Dalvi, F., Alam, F., Khan, A., & Xu, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.225 · Cited by 24
Metadata
- Authors
- Chidananda Gowda, K., & Krishna, G.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 1978-01
- Volume
- 10
- Issue
- 2
- Pages
- 105-112
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2020735245
- Cited by
- 405
- References
- 0
- FWCI
- 4.68
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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: Agglomerative clustering using the concept of mutual nearest neighbourhood. https://app.cochat.ai/papers/10.1016/0031-3203(78)90018-3
- The paper
- Chidananda Gowda, K., & Krishna, G. (1978). Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition. https://doi.org/10.1016/0031-3203(78)90018-3
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.
