Weighted Clustering

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Dokumenter

  • Margareta Ackerman, University of Waterloo, Canada
  • Shai Ben-David, University of Waterloo, Canada
  • Simina Branzei
  • David Loker, University of Waterloo, Canada
We investigate a natural generalization of the classical clustering
problem, considering clustering tasks in which different
instances may have different weights.We conduct the first
extensive theoretical analysis on the influence of weighted
data on standard clustering algorithms in both the partitional
and hierarchical settings, characterizing the conditions under
which algorithms react to weights. Extending a recent framework
for clustering algorithm selection, we propose intuitive
properties that would allow users to choose between clustering
algorithms in the weighted setting and classify algorithms
accordingly.
OriginalsprogEngelsk
TitelProceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Antal sider6
ForlagAAAI Press
Udgivelsesår2012
Sider858-863
ISBN (trykt) 978-1-57735-568-7
StatusUdgivet - 2012
BegivenhedAAAI - Toronto, Canada
Varighed: 22 jul. 201226 jul. 2012

Konference

KonferenceAAAI
LandCanada
ByToronto
Periode22/07/201226/07/2012

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