Multiple Clustering Views via Constrained Projections

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  • MultiClust

    Submitted manuscript, 176 KB, PDF document

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  • Xuan-Hong Dang, Denmark
  • Ira Assent
  • James Bailey, Department of Computing and Information Systems, The University of Melbourne, Australia
Clustering, the grouping of data based on mutual similarity,
is often used as one of principal tools to analyze and understand
data. Unfortunately, most conventional techniques
aim at finding only a single clustering over the data. For
many practical applications, especially those being described
in high dimensional data, it is common to see that the data
can be grouped into different yet meaningful ways. This
gives rise to the recently emerging research area of discovering
alternative clusterings. In this preliminary work, we
propose a novel framework to generate multiple clustering
views. The framework relies on a constrained data projection
approach by which we ensure that a novel alternative
clustering being found is not only qualitatively strong but
also distinctively different from a reference clustering solution.
We demonstrate the potential of the proposed framework
using both synthetic and real world datasets and discuss
some future research directions with the approach.
Original languageEnglish
Title of host publication2012 SIAM International Conference on Data Mining : 3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings
EditorsEmmanuel Müller, Thomas Seidl, Arthur Zimek
Number of pages8
PublisherSociety for Industrial and Applied Mathematics
Publication year2012
Pages23-30
Publication statusPublished - 2012
EventMultiClust Workshop - Anaheim, United States
Duration: 28 Apr 201228 Apr 2012
Conference number: 3

Conference

ConferenceMultiClust Workshop
Nummer3
LandUnited States
ByAnaheim
Periode28/04/201228/04/2012

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