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A Novel Center Point Initialization Technique for K-means Clustering Algorithm

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  • Dauda Usman
  • Ismail Mohamad

Abstract

Clustering is a major data analysis tool utilized in numerous domains. The basic K-means method has been widely discussed and applied in many applications. But unfortunately failed to offer good clustering result due to the initial center points are chosen randomly. In this article, we present a new method of centre points initialization and we prove that the distance of the new method follows a Chi-square distribution. The new method overcomes the drawbacks of the basic K-means. Experimental analysis shows that the new method performs well on infectious diseases dataset when compare with the basic K-means clustering method and a histogram measures the quality of the new method.

Suggested Citation

  • Dauda Usman & Ismail Mohamad, 2013. "A Novel Center Point Initialization Technique for K-means Clustering Algorithm," Modern Applied Science, Canadian Center of Science and Education, vol. 7(9), pages 1-10, September.
    • Handle: RePEc:ibn:masjnl:v:7:y:2013:i:9:p:10
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    References listed on IDEAS

    as
    1. Glenn Milligan & Martha Cooper, 1988. "A study of standardization of variables in cluster analysis," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 181-204, September.
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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