Skip to main content

An Enhancing K-Means Algorithm Based on Sorting and Partition

  • Conference paper
Information Computing and Applications (ICICA 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 391))

Included in the following conference series:

  • 1571 Accesses

Abstract

The accuracy and efficiency as the two main evaluation indexes for k-means algorithm are influenced by the choice of initial clustering centers and the partition method of data points. In this paper, in view of the deficiency of direct k-means algorithm which chooses initial centers randomly, we propose a novel method about initial clustering centers based on sorting and partition and apply it to real data as well as simulated data, which show that this is an efficient method to improve the clustering accuracy and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Xu, R., Wunsch Il, D.: Survey of clustering algorithms. IEEE Trans. on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  2. Tou, Gonzaales, R.: Pattern Recognition Principles, pp. 54–57. Addison-Wesley, New Jersey (1974)

    Google Scholar 

  3. Linde, Y., Buzo, A., Gray, R.M.: An algorithm for vector quantizer design. IEEE Transaction on Communication 28(2), 84–95 (1980)

    Article  Google Scholar 

  4. Babu, G.P.: A near-optimal initial seed value selection in k-means algorithm using a genetic algorithm. Pattern Recognition Lett. 14(10), 763–769 (1993)

    Article  MATH  Google Scholar 

  5. Huang, C., Harris, R.: A comparison of several codebook generation approaches. IEEE Transaction on Image Process. 2(1), 108–112 (1993)

    Google Scholar 

  6. Khan, S.S., Ahmad, A.: Cluster center initialization algorithm for k-means clustering. Pattern Recognition Letters 25(11), 1293–1302 (2004)

    Article  Google Scholar 

  7. Birgin, E.G., Martinez, J.M., Ronconi, D.P.: Minimization Subproblems and Heuristics for an Applied Clustering Problem. European Journal of Operational Research 146(1), 19–34 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  8. Corral, A., Alnendros, J.M.: A Performance Comparison of Distance-Based Query Algorithms Using R-Trees in Spatial Databases Information Sciences. An International Journal 177(11), 2207–2237 (2007)

    MathSciNet  Google Scholar 

  9. Kangngo, T., Mount, D.M., Netanyaha, N.S.: An Efficient K-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 881–892 (2002)

    Article  Google Scholar 

  10. Redmod, S.J., Heneghan, C.: A method for initializing the K-means clustering algorithm using kd-trees. Pattern Recognition Letters 28, 965–973 (2007)

    Article  Google Scholar 

  11. Fahim, A.M., Salem, A.M., Torkey, A., Ramadan, M.A.: An Efficient enhanced k-means clustering algorithm. Journal of Zhejiang University 10(7), 1626–1633

    Google Scholar 

  12. Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Trans. Neural Networks 5(1), 3–14 (1994)

    Article  Google Scholar 

  13. Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. of Michigan Press (1975)

    Google Scholar 

  14. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall (1989)

    Google Scholar 

  15. Klein, R.W., Dubes, R.C.: Experiments in projection and clustering by simulated annealing. Pattern Recognit. 22, 213–220 (1989)

    Article  MATH  Google Scholar 

  16. Selim, S.Z., Alsultan, K.: A simulated annealing algorithm for the clustering problem. Pattern Recognit. 10(24), 1003–1008 (1991)

    Article  MathSciNet  Google Scholar 

  17. Babu, G.P., Murty, M.N.: Simulated annealing for selecting initial seeds in the k-means algorithm. Ind. J. Pure Appl. Math. 25, 85–94 (1994)

    MATH  Google Scholar 

  18. Bhuyan, J.N., Raghavan, V.V., Elayavalli, V.K.: Genetic algorithm for clustering with an ordered representation. In: Proc. 4th Int. Conf. Genetic Algorithms. Morgan Kaufmann (1991)

    Google Scholar 

  19. Jones, D.R., Beltramo, M.A.: Solving partitioning problems with genetic algorithms. In: Proc. 4th Int. Conf. Genetic Algorithms. Morgan Kaufmann (1991)

    Google Scholar 

  20. Babu, G.P., Murty, M.N.: A near-optimal initial seed selection in K-means algorithm using a genetic algorithm. Pattern Recognit. Lett. 14, 763–769 (1993)

    Article  MATH  Google Scholar 

  21. Babu, G.P., Murty, M.N.: Clustering with evolution strategies. Pattern Recognit. 27(2), 321–329 (1994)

    Article  Google Scholar 

  22. Babu, G.P.: Connectionist and evolutionary approaches for pattern clustering, Dept. Comput. Sci. Automat., Indian Inst. Sci. (1994)

    Google Scholar 

  23. Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley (1989)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jun-wei, Y., Jian-ming, C., Bai-li, X., Jian, Z. (2013). An Enhancing K-Means Algorithm Based on Sorting and Partition. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53932-9_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-53932-9_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53931-2

  • Online ISBN: 978-3-642-53932-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics