Fast K-means clustering using deletion by center displacement and norms product (CDNP)

Suiang Shyan Lee, Chih-Ching Lin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

K-means clustering method has been widely used in many areas. However, it is time-consuming when data are in high dimensional space, or when there are many clusters. We try to accelerate its speed by combing our previous work with the simplest version of a fast K-means method that gracefully used the centers' displacement between two iterations. Experimental results show our method not only is several times faster than the fast K-Means method using center displacement; but also accelerates the fast K-means method that used norms-product test only. As a result, the proposed hybrid method is much faster than the plain K-means method. Hence, it is very useful in real-time data mining; examples include medical diagnostics, customer analysis, and vector quantization.

Original languageEnglish
Pages (from-to)199-206
Number of pages8
JournalPattern Recognition and Image Analysis
Volume23
Issue number2
DOIs
StatePublished - 1 Apr 2013

Keywords

  • K-means clustering
  • acceleration
  • deletion using center displacement
  • deletion using norms product
  • real-time data mining
  • vector quantization

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