A Clustering Method Based on K-Means Algorithm

In this paper we combine the largest minimum distance algorithm and the traditional K-Means algorithm to propose an improved K-Means clustering algorithm. This improved algorithm can make up the shortcomings for the traditional K-Means algorithm to determine the initial focal point. The improved K-M...

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Published inPhysics procedia Vol. 25; pp. 1104 - 1109
Main Authors Li, Youguo, Wu, Haiyan
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2012
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Abstract In this paper we combine the largest minimum distance algorithm and the traditional K-Means algorithm to propose an improved K-Means clustering algorithm. This improved algorithm can make up the shortcomings for the traditional K-Means algorithm to determine the initial focal point. The improved K-Means algorithm effectively solved two disadvantages of the traditional algorithm, the first one is greater dependence to choice the initial focal point, and another one is easy to be trapped in local minimum[1,2].
AbstractList In this paper we combine the largest minimum distance algorithm and the traditional K-Means algorithm to propose an improved K-Means clustering algorithm. This improved algorithm can make up the shortcomings for the traditional K-Means algorithm to determine the initial focal point. The improved K-Means algorithm effectively solved two disadvantages of the traditional algorithm, the first one is greater dependence to choice the initial focal point, and another one is easy to be trapped in local minimum[1,2].
Author Wu, Haiyan
Li, Youguo
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Keywords distance algorithm
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K-Means algorithm
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References BianZhaoQi, ZhangXuegong (bib0015) 2000
JAIN.A.K., DUBE.S.R.C. (bib0020) 1988
SELIM.S.Z., ISMAI.L.M. (bib0030) 1984; 6.
SunXin, YuAnping (bib0035) 2006
Usama, Fayyad cory, Reina Paul, Bradley (bib0005) 1998
DUDA.R.O., HAR.T.P.E. (bib0010) 1973
ZhangYufang (bib0025) 2003; 8
JAIN.A.K. (10.1016/j.phpro.2012.03.206_bib0020) 1988
Usama (10.1016/j.phpro.2012.03.206_bib0005) 1998
SELIM.S.Z. (10.1016/j.phpro.2012.03.206_bib0030) 1984; 6.
DUDA.R.O. (10.1016/j.phpro.2012.03.206_bib0010) 1973
ZhangYufang (10.1016/j.phpro.2012.03.206_bib0025) 2003; 8
BianZhaoQi (10.1016/j.phpro.2012.03.206_bib0015) 2000
SunXin (10.1016/j.phpro.2012.03.206_bib0035) 2006
References_xml – year: 1973
  ident: bib0010
  article-title: Pattern classification and scene analysis
– volume: 8
  start-page: 31
  year: 2003
  end-page: 33
  ident: bib0025
  article-title: A kind of improved K-means algorithm [J]
  publication-title: Computer Application
– year: 2006
  ident: bib0035
  article-title: VC++ in depth detailed introduction
– volume: 6.
  start-page: 81
  year: 1984
  end-page: 87
  ident: bib0030
  article-title: A.K-means type algorithms: a generalized convergence theorem and characterization of local optimality [J]
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
– year: 1998
  ident: bib0005
  publication-title: Initialization of Iterative Refinement clustering algorithms[C]. Proc. 4th International Conf. On Knowledge Discovery & Data Mining
– year: 2000
  ident: bib0015
  article-title: Pattern Recognition
– year: 1988
  ident: bib0020
  article-title: Algorithms for clustering data
– volume: 8
  start-page: 31
  year: 2003
  ident: 10.1016/j.phpro.2012.03.206_bib0025
  article-title: A kind of improved K-means algorithm [J]
  publication-title: Computer Application
– year: 2006
  ident: 10.1016/j.phpro.2012.03.206_bib0035
– year: 2000
  ident: 10.1016/j.phpro.2012.03.206_bib0015
– year: 1973
  ident: 10.1016/j.phpro.2012.03.206_bib0010
– volume: 6.
  start-page: 81
  issue: 1
  year: 1984
  ident: 10.1016/j.phpro.2012.03.206_bib0030
  article-title: A.K-means type algorithms: a generalized convergence theorem and characterization of local optimality [J]
  publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence
  doi: 10.1109/TPAMI.1984.4767478
– year: 1998
  ident: 10.1016/j.phpro.2012.03.206_bib0005
  publication-title: Initialization of Iterative Refinement clustering algorithms[C]. Proc. 4th International Conf. On Knowledge Discovery & Data Mining
– year: 1988
  ident: 10.1016/j.phpro.2012.03.206_bib0020
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Snippet In this paper we combine the largest minimum distance algorithm and the traditional K-Means algorithm to propose an improved K-Means clustering algorithm. This...
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SubjectTerms cluster analysis
distance algorithm
K-Means algorithm
samples of pattern
Title A Clustering Method Based on K-Means Algorithm
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