The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A simple and fast algorithm for K-medoids clustering
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“The most common clustering algorithms based on partitioning methods are K-means [69], K-Mediods [80], CLARA [55] etc.”
From Machine learning: Algorithms, real-world applications and research directions (2021) · cites this paper as [80]
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Citations
Cites
(1967) · Cited by 23,719
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV
Roy, H. L. L., Lecam, L., & Neyman, J. (1969) · Revue de l'Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1402306 · Cited by 2,835
Efficient and Effective Clustering Methods for Spatial Data Mining
Ng, R. T., & Han, J. (1994) · Cited by 1,778
Cluster center initialization algorithm for K-means clustering
Khan, S. S., & Ahmad, A. (2004) · Pattern Recognition Letters · doi:10.1016/j.patrec.2004.04.007 · Cited by 685
Geographic Data Mining and Knowledge Discovery
Miller, H. J. (2007) · The Handbook of Geographic Information Science · doi:10.1002/9780470690819.ch19 · Cited by 617
Spatial clustering methods in data mining : A survey
Han, J. (2001) · Cited by 385
A new partitioning around medoids algorithm
Van der Laan, M., Pollard, K., & Bryan, J. (2003) · Journal of Statistical Computation and Simulation · doi:10.1080/0094965031000136012 · Cited by 287
Research works printed but not linked: 1
- Wei, 2003, Empirical comparison of fast partitioning-based clustering algorithms for large data sets, Expert Systems with Applications, 24, 351, 10.1016/S0957-4174(02)00185-9 · doi:10.1016/s0957-4174(02)00185-9
Cited by
Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00592-x · Cited by 5,583
Molecular Docking: Shifting Paradigms in Drug Discovery
Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,344
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762
Sarker, I. H. (2022) · SN Computer Science · doi:10.1007/s42979-022-01043-x · Cited by 1,208
Metadata
- Authors
- Park, H.-S., & Jun, C.-H.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-03
- Volume
- 36
- Issue
- 2
- Pages
- 3336-3341
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2083620785
- Cited by
- 2,104
- References
- 13
- FWCI
- 17.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- CoChat. (2026). Citation check: A simple and fast algorithm for K-medoids clustering. https://app.cochat.ai/papers/10.1016/j.eswa.2008.01.039
- The paper
- Park, H.-S., & Jun, C.-H. (2009). A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2008.01.039
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.
