Advertisement

BK-means Algorithm with Minimal Performance Degradation Caused by Improper Initial Centroid

  • Hoon JoEmail author
  • Soon-cheol Park
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 274)

Abstract

K-means algorithm has the performance degradation problem due to improper initial centroids. In order to solve the problem, we suggest BK-means (Balanced K-means) algorithm to cluster documents. This algorithm uses the value, α, to adjust each cluster weight which is first defined in this paper. We compared the algorithm to the general K-means algorithms on Reutor-21578. The experimental results show about 11% higher performance than that of the general K-means algorithm with the balanced F Measure (BFM).

Keywords

Clustering Information Retrieval K-means BK-means Outlier 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics Philadelphia (2007)Google Scholar
  2. 2.
    Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press (2008)Google Scholar
  3. 3.
    Liu, T., Liu, S., Chen, Z., Ma, W.Y.: An evaluation on feature selection for text clustering. In: Proceedings of the Twentieth International Conference, pp. 488–495. AAAI Press, Washington, DC (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Division of Electronics and Information Engineering DepartmentChonbuk National UniversityJeonjuKorea
  2. 2.Division of Electronics and Information Engineering Department and IT Convergence Research CenterChonbuk National UniversityJeonjuKorea

Personalised recommendations