Advertisement

An Expanding Clustering Algorithm Based on Density Searching

  • Liguo Tan
  • Yang Liu
  • Xinglin Chen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 236)

Abstract

Most clustering algorithms need to preset the initial parameters which affect the performance of clustering very much. To solve this problem, a new method is proposed, which determine the center points of clustering by density-searching according to the universality of the Gaussian distribution. After the center was obtained, the cluster expands based on the correlation coefficient between clusters and the membership of the samples until the terminating condition is met. The experimental results show that this method could classify the samples of Gaussian distribution with different degree of overlap accurately. Compared with the fuzzy c-means algorithm, the proposed method is more accurate and timesaving when applied to the Iris data and Fossil data.

Keywords

clustering density searching clustering center Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hu, B.-p., He, X.-s.: Novel BSS algorithm for estimating PDF based on SVM. Computer Engineering and Applications 45(17), 142–144 (2009)Google Scholar
  2. 2.
    Berkhin, P.: A Survey of clustering data mining techniques. In: Koganand, J., Nieholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data: Recent Advances in Clustering, pp. 25–71. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Yu, F.-x., Su, J.-y., Lu, Z.-m., et al.: Multi-feature based fire detection in video. International Journal of Innovative Computing, Information and Control 4(8), 1987–1993 (2008)Google Scholar
  4. 4.
    Lei, X.-F., Xie, K.-Q., Lin, F.: An Efficient Clustering Algorithm Based on Local Optimality of K-Means. Journal of Software 7(19), 1683–1692 (2008)CrossRefzbMATHGoogle Scholar
  5. 5.
    Jiang, S.-y., Li, X.: Improved BIRCH clustering algorithm. Journal of Computer Applications 29(1), 293–296 (2009)CrossRefzbMATHGoogle Scholar
  6. 6.
    Zhou, X.-Y., Zhang, J., Sun, Z.-H.: An Efficient Clustering Algorithm for High Dimensional Turnstile Data Streams. Computer Science 33(11), 14–17 (2006)Google Scholar
  7. 7.
    Strehl, A., Ghosh, J.: Relationship-based Clustering and Visualization for High-dimensional Data Mining. Informs Journal on Computing 15(2), 208–230 (2003)CrossRefzbMATHGoogle Scholar
  8. 8.
    xu, R., Wunsch, D.: Survey of clustering algorithms. Transactions on Neural Networks 16(3), 645–678 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liguo Tan
    • 1
  • Yang Liu
    • 1
  • Xinglin Chen
    • 1
  1. 1.School of Automation Science and EngineeringHarbin Institute of TechnologyHarbinChina

Personalised recommendations