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Weighted Clustering

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Documents

  • 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.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence
Number of pages6
PublisherAAAI Press
Publication year2012
Pages858-863
ISBN (print) 978-1-57735-568-7
Publication statusPublished - 2012
EventAAAI - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012

Conference

ConferenceAAAI
LandCanada
ByToronto
Periode22/07/201226/07/2012

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