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Data clustering: a review

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Jain, A. K., Murty, M. N., Flynn, P. J.·1999·ACM Computing Surveys
DOI 10.1145/331499.331504
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Abstract

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

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Cited by

Metadata

Authors
Jain, A. K., Murty, M. N., & Flynn, P. J.
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
1999-09
Volume
31
Issue
3
Pages
264-323
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/331499.331504
OpenAlex
W1992419399
Cited by
13,199
References
4
FWCI
70.39
Type
journal article
Language
en
Open access
bronze
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)

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.

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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.