A Creditable Subspace Labeling Method Based on D-S Evidence Theory

  • Yu Zong
  • Xian-Chao Zhang
  • He Jiang
  • Ming-Chu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5012)


Due to inherent sparse, noise and nearly zero difference characteristics of high dimensional data sets, traditional clustering methods fails to detect meaningful clusters in them. Subspace clustering attempts to find the true distribution inherent to the subsets with original attributes. However, which subspace contains the true clustering result is usually uncertain. From this point of view, subspace clustering can be regarded as an uncertain discursion problem. In this paper, we firstly develop the criterion to evaluate creditable subspaces which contain the meaningful clustering results, and then propose a creditable subspace labeling method (CSL) based on D-S evidence theory. The creditable subspaces of the original data space can be found by iteratively executing the algorithm CSL. Once the creditable subspaces are got, the true clustering results can be found using a traditional clustering algorithm on each creditable subspace. Experiments show that CSL can detect the actual creditable subspace with the original attribute. In this way, a novel approach of clustering problems using traditional clustering algorithms to deal with high dimension data sets is proposed.


Cluster Algorithm Subspace Cluster Evidence Theory Spatial Data Mining Original Data Space 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yu Zong
    • 1
  • Xian-Chao Zhang
    • 1
  • He Jiang
    • 1
  • Ming-Chu Li
    • 1
  1. 1.School of SoftwareDalian University of TechnologyDalianChina

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