The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Why so many clustering algorithms: a position paper
Abstract
We argue that there are many clustering algorithms, because the notion of "cluster" cannot be precisely defined. Clustering is in the eye of the beholder, and as such, researchers have proposed many induction principles and models whose corresponding optimization problem can only be approximately solved by an even larger number of algorithms. Therefore, comparing clustering algorithms, must take into account a careful understanding of the inductive principles involved.
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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
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Pattern Recognition with Fuzzy Objective Function Algorithms
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Cited by
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
Big IoT data analytics: Architecture, opportunities, and open research challenges
(2017) · IEEE Access · doi:10.1109/access.2017.2689040 · Cited by 980
RapidMiner: Data Mining Use Cases and Business Analytics Applications
(2016) · Chapman & Hall/CRC eBooks · doi:10.1201/b16023 · Cited by 314
Metadata
- Authors
- Estivill-Castro, V.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2002-06
- Volume
- 4
- Issue
- 1
- Pages
- 65-75
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2054274648
- Cited by
- 600
- References
- 52
- FWCI
- 12.38
- Type
- journal article
- Language
- en
- Open access
- closed
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- Estivill-Castro, V. (2002). Why so many clustering algorithms: a position paper. ACM SIGKDD Explorations Newsletter. https://doi.org/10.1145/568574.568575
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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.
