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On clusterings: Good, bad and spectral

Kannan, Ravi, Vempala, Santosh, Vetta, Adrian·2004·Journal of the ACM

Abstract

We motivate and develop a natural bicriteria measure for assessing the quality of a clustering that avoids the drawbacks of existing measures. A simple recursive heuristic is shown to have poly-logarithmic worst-case guarantees under the new measure. The main result of the article is the analysis of a popular spectral algorithm. One variant of spectral clustering turns out to have effective worst-case guarantees; another finds a "good" clustering, if one exists.

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Metadata

Authors
Kannan, R., Vempala, S., & Vetta, A.
Venue
Journal of the ACM
Published
2004-05
Volume
51
Issue
3
Pages
497-515
ISSN
  • 0004-5411 (ISSN-L)
  • 1557-735X
OpenAlex
W2034331023
Cited by
834
References
0
FWCI
34.57
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: On clusterings: Good, bad and spectral. https://app.cochat.ai/papers/10.1145/990308.990313
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Kannan, R., Vempala, S., & Vetta, A. (2004). On clusterings: Good, bad and spectral. Journal of the ACM. https://doi.org/10.1145/990308.990313

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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.