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Initialization dependence of clustering algorithms

Wim De Mulder UGent, Stefan Schliebs, René Boel UGent and Martin Kuiper (2009) LECTURE NOTES IN COMPUTER SCIENCE. 5507. p.615-622
abstract
It is well known that the clusters produced by a clustering algorithm depend on the chosen initial centers. In this paper we present a measure for the degree to which a given clustering algorithm depends on the choice of initial centers, for a given data set. This measure is calculated for four well-known offline clustering algorithms (k-means Forgy, k-means Hartigan, k-means Lloyd and frizzy c-means), for five benchmark data sets. The measure is also calculated for ECM, an online algorithm that does not require the number of initial centers as input, but for which the resulting clusters can depend oil the order that the input arrives. Our main finding is that this initialization dependence measure call also he used to determine the optimal number of clusters.
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author
organization
year
type
conference
publication status
published
subject
in
LECTURE NOTES IN COMPUTER SCIENCE
Lect. Notes Comput. Sci.
editor
Mario Köppen, Nikola Kasabov and George Coghill
volume
5507
issue title
ADVANCES IN NEURO-INFORMATION PROCESSING, PT II
pages
615 - 622
publisher
Springer
place of publication
Berlin, Germany
conference name
15th International Conference on Neuro-Information Processing
conference location
Auckland, New Zealand
conference start
2008-11-25
conference end
2008-11-28
Web of Science type
Proceedings Paper
Web of Science id
000270578200075
ISSN
0302-9743
ISBN
978-3-642-03039-0
DOI
10.1007/978-3-642-03040-6_75
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
790422
handle
http://hdl.handle.net/1854/LU-790422
date created
2009-11-26 22:13:09
date last changed
2010-07-30 11:17:21
@inproceedings{790422,
  abstract     = {It is well known that the clusters produced by a clustering algorithm depend on the chosen initial centers. In this paper we present a measure for the degree to which a given clustering algorithm depends on the choice of initial centers, for a given data set. This measure is calculated for four well-known offline clustering algorithms (k-means Forgy, k-means Hartigan, k-means Lloyd and frizzy c-means), for five benchmark data sets. The measure is also calculated for ECM, an online algorithm that does not require the number of initial centers as input, but for which the resulting clusters can depend oil the order that the input arrives. Our main finding is that this initialization dependence measure call also he used to determine the optimal number of clusters.},
  author       = {De Mulder, Wim and Schliebs, Stefan and Boel, Ren{\'e} and Kuiper, Martin},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {K{\"o}ppen, Mario and Kasabov, Nikola and Coghill, George},
  isbn         = {978-3-642-03039-0},
  issn         = {0302-9743},
  language     = {eng},
  location     = {Auckland, New Zealand},
  pages        = {615--622},
  publisher    = {Springer},
  title        = {Initialization dependence of clustering algorithms},
  url          = {http://dx.doi.org/10.1007/978-3-642-03040-6\_75},
  volume       = {5507},
  year         = {2009},
}

Chicago
De Mulder, Wim, Stefan Schliebs, René Boel, and Martin Kuiper. 2009. “Initialization Dependence of Clustering Algorithms.” In Lecture Notes in Computer Science, ed. Mario Köppen, Nikola Kasabov, and George Coghill, 5507:615–622. Berlin, Germany: Springer.
APA
De Mulder, W., Schliebs, S., Boel, R., & Kuiper, M. (2009). Initialization dependence of clustering algorithms. In M. Köppen, N. Kasabov, & G. Coghill (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 5507, pp. 615–622). Presented at the 15th International Conference on Neuro-Information Processing, Berlin, Germany: Springer.
Vancouver
1.
De Mulder W, Schliebs S, Boel R, Kuiper M. Initialization dependence of clustering algorithms. In: Köppen M, Kasabov N, Coghill G, editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Germany: Springer; 2009. p. 615–22.
MLA
De Mulder, Wim, Stefan Schliebs, René Boel, et al. “Initialization Dependence of Clustering Algorithms.” Lecture Notes in Computer Science. Ed. Mario Köppen, Nikola Kasabov, & George Coghill. Vol. 5507. Berlin, Germany: Springer, 2009. 615–622. Print.