[Lisa_seminaires] [CRM-L'officiel] COLLOQUE CRM-ISM-GERAD DE STATISTIQUE: R. Nugent (Le vendredi 26 octobre 2007) (fwd)
Sébastien Gambs
gambsseb at iro.umontreal.ca
Jeu 25 Oct 22:57:27 EDT 2007
Bonjour,
J'ai pensé que cela pourrait sûrement intéresser certaines personnes du
lab. Désolé pour l'annonce tardive, la conférence a lieu demain à 15h30
à McGill.
Bonne soirée,
Sébastien
From: CRM <activites at CRM.UMontreal.CA>
To: lofficiel <lofficiel at CRM.UMontreal.CA>
Subject: [CRM-L'officiel] COLLOQUE CRM-ISM-GERAD DE STATISTIQUE: R.
Nugent (Le
vendredi 26 octobre 2007)
RAPPEL - REMINDER - RAPPEL - REMINDER - RAPPEL
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COLLOQUE CRM-ISM-GERAD DE STATISTIQUE
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CONFERENCIER(S) / SPEAKER(S) :
Rebecca Nugent
(Carnegie Mellon University)
TITRE / TITLE :
"Visualizing Clusters With a Density-Based Similarity Measure"
LIEU / PLACE :
McGill, Burnside Hall, 805 Sherbrooke O., BH 1B45
DATE :
Le vendredi 26 octobre 2007 / Friday, October 26, 2007
HEURE / TIME :
15 h 30 / 3:30 p.m.
RESUME / ABSTRACT :
The goal of clustering is to identify distinct groups in a dataset and
assign a group label to each observation. To cast clustering as a
statistical problem, we regard the data as a sample from an unknown
density p(x). To generate clusters, we estimate the properties of p(x)
either with parametric (model-based) or nonparametric methods. In
contrast, the algorithmic approach to clustering (linkage methods,
spectral clustering) applies an algorithm, often based on a distance
measure, to data in m-dimensional space. Many commonly used clustering
methods employ functions of Euclidean distance between observations to
determine groupings. Spherical groups are easily identified, curvilinear
groups less so. We first motivate the use of a density-based similarity
measure and briefly introduce generalized single linkage, a graph-based
clustering approach. We describe a refinement algorithm used to bound
the measure and then explore the performance of this measure in
clustering and visualization methods.
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http://www.crm.umontreal.ca/cgi/Stats.csh
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