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)


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.


clustering density searching clustering center Algorithm 


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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

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