Speeding-Up the K-Means Clustering Method: A Prototype Based Approach

  • T. Hitendra Sarma
  • P. Viswanath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


The paper is about speeding-up the k-means clustering method which processes the data in a faster pace, but produces the same clustering result as the k-means method. We present a prototype based method for this where prototypes are derived using the leaders clustering method. Along with prototypes called leaders some additional information is also preserved which enables in deriving the k means. Experimental study is done to compare the proposed method with recent similar methods which are mainly based on building an index over the data-set.


Cluster Method Leader Method Density Base Cluster Method Partition Base Method Pattern Recognition Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Bradley, P.S., Fayyad, U., Raina, C.: Scaling clustering algorithms to large databases. In: Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining, pp. 9–15. AAAI Press, Menlo Park (1998)Google Scholar
  2. 2.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999)CrossRefGoogle Scholar
  3. 3.
    Kanungo, T., Mount, D.M., Netanyahu, N.S.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)CrossRefGoogle Scholar
  4. 4.
    Lloyd, S.P.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 129–137 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Spath, H.: Cluster Analysis Algorithms for Data Reduction and Classification. Ellis Horwood, Chichester (1980)Google Scholar
  6. 6.
    Babu, V.S., Viswanath, P.: Rough-fuzzy weighted k-nearest leader classifier for large data sets. Pattern Recognition 42, 1719–1731 (2009)zbMATHCrossRefGoogle Scholar
  7. 7.
    Viswanath, P., Suresh Babu, V.: Rough-DBSCAN: A fast hybrid density based clustering mehtod for large data sets. Pattern Recognition latters (2009) doi:10.1016/j.patrec.2009.08.008Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • T. Hitendra Sarma
    • 1
  • P. Viswanath
    • 1
  1. 1.Pattern Recognition Research LaboratoryNRI Institute of TehnologyGunturIndia

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