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An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

k-means clustering is a popular clustering method.Its core task of finding the closest prototype for every input pattern involves expensive distance calculations. We present a novel algorithm for performing this task. This and other optimizations are shown to significantly improve the performance of the k-means algorithm. The resultant algorithm produces the same (except for round-off errors)results as those of the direct algorithm.

A large part of this work was done while the authors at the Information Technology Lab (ITL)of Hitachi America Ltd.

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References

  1. K. AlSabti. Efficient Algorithms for Data Mining. Ph.D. Thesis, Syracuse University, 1998.

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  2. R.C. Dubes and A.K. Jain. Algorithms fo Clustering Data. Prentice Hall, 1988.

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  3. D. Judd, P. McKinley and A. Jain. Large-Scale Parallel Data Clustering. Proc. Int’l Conference on Pattern Recognition, August 1996.

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  4. K. Mehrotra C.K. Mohan, and S. Ranka. Elements of Artificial Neural Networks. MIT Press, 1996.

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  5. T. Zhang, R. Ramakrishnan and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proc.of the ACM SIGMOD’f96.

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Author information

Authors and Affiliations

  1. Department of CS, King Saud University, USA

    Khaled AlSabti

  2. Department of CISE, University of Florida, USA

    Sanjay Ranka

  3. Hitachi America Ltd., USA

    Vineet Singh

Authors
  1. Khaled AlSabti
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  2. Sanjay Ranka
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  3. Vineet Singh
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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AlSabti, K., Ranka, S., Singh, V. (1999). An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_47

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  • DOI: https://doi.org/10.1007/3-540-48912-6_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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