# A graph-based estimator of the number of clusters

• Volume: 11, page 272-280
• ISSN: 1292-8100

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## Abstract

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Assessing the number of clusters of a statistical population is one of the essential issues of unsupervised learning. Given n independent observations X1,...,Xn drawn from an unknown multivariate probability density f, we propose a new approach to estimate the number of connected components, or clusters, of the t-level set $ℒ\left(t\right)=\left\{x:f\left(x\right)\ge t\right\}$. The basic idea is to form a rough skeleton of the set $ℒ\left(t\right)$ using any preliminary estimator of f, and to count the number of connected components of the resulting graph. Under mild analytic conditions on f, and using tools from differential geometry, we establish the consistency of our method.

## How to cite

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Biau, Gérard, Cadre, Benoît, and Pelletier, Bruno. "A graph-based estimator of the number of clusters." ESAIM: Probability and Statistics 11 (2007): 272-280. <http://eudml.org/doc/250119>.

@article{Biau2007,
abstract = { Assessing the number of clusters of a statistical population is one of the essential issues of unsupervised learning. Given n independent observations X1,...,Xn drawn from an unknown multivariate probability density f, we propose a new approach to estimate the number of connected components, or clusters, of the t-level set $\mathcal L(t)=\\{x:f(x) \geq t\\}$. The basic idea is to form a rough skeleton of the set $\mathcal L(t)$ using any preliminary estimator of f, and to count the number of connected components of the resulting graph. Under mild analytic conditions on f, and using tools from differential geometry, we establish the consistency of our method. },
author = {Biau, Gérard, Cadre, Benoît, Pelletier, Bruno},
journal = {ESAIM: Probability and Statistics},
keywords = {Cluster analysis; connected component; level set; graph; tubular neighborhood.; cluster analysis; tubular neighborhood},
language = {eng},
month = {6},
pages = {272-280},
publisher = {EDP Sciences},
title = {A graph-based estimator of the number of clusters},
url = {http://eudml.org/doc/250119},
volume = {11},
year = {2007},
}

TY - JOUR
AU - Biau, Gérard
AU - Pelletier, Bruno
TI - A graph-based estimator of the number of clusters
JO - ESAIM: Probability and Statistics
DA - 2007/6//
PB - EDP Sciences
VL - 11
SP - 272
EP - 280
AB - Assessing the number of clusters of a statistical population is one of the essential issues of unsupervised learning. Given n independent observations X1,...,Xn drawn from an unknown multivariate probability density f, we propose a new approach to estimate the number of connected components, or clusters, of the t-level set $\mathcal L(t)=\{x:f(x) \geq t\}$. The basic idea is to form a rough skeleton of the set $\mathcal L(t)$ using any preliminary estimator of f, and to count the number of connected components of the resulting graph. Under mild analytic conditions on f, and using tools from differential geometry, we establish the consistency of our method.
LA - eng
KW - Cluster analysis; connected component; level set; graph; tubular neighborhood.; cluster analysis; tubular neighborhood
UR - http://eudml.org/doc/250119
ER -

## References

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