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Clustering by fast search-and-find of density peaks

Rodriguez Alex, A.
•
Laio, Alessandro
2014
  • journal article

Periodico
SCIENCE
Abstract
Cluster analysis is aimed at classifying elements into categories on the basis of their similarity. Its applications range from astronomy to bioinformatics, bibliometrics, and pattern recognition.We propose an approach based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher densities. This idea forms the basis of a clustering procedure in which the number of clusters arises intuitively, outliers are automatically spotted and excluded fromthe analysis, and clusters are recognized regardless of their shape and of the dimensionality of the space inwhich they are embedded.We demonstrate the power of the algorithm on several test cases.
DOI
10.1126/science.1242072
WOS
WOS:000337889900044
Archivio
http://hdl.handle.net/20.500.11767/14183
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84903289127
Diritti
open access
Soggetti
  • algorithm

  • cluster analysi

  • numerical method

  • outlier

  • performance assessmen...

  • algorithm

  • article

  • cluster analysi

  • priority journal

  • algorithm

  • artificial intelligen...

  • automated pattern rec...

  • factual database

  • Algorithm

  • Artificial Intelligen...

  • Cluster Analysi

  • Databases, Factual

  • Pattern Recognition, ...

  • Settore FIS/03 - Fisi...

Scopus© citazioni
2939
La settimana scorsa
2
Data di acquisizione
Jun 14, 2022
Vedi dettagli
Web of Science© citazioni
3254
Data di acquisizione
Mar 28, 2024
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