Clustering Mixed Data Based on Evidence Accumulation

  • Huilan Luo
  • Fansheng Kong
  • Yixiao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity matrix to extract a partition of the data. The performance of EBSC has been studied on real data sets. Results demonstrate the effectiveness of this algorithm in clustering mixed data tasks. Comparisons with other related clustering schemes illustrate the superior performance of this approach.


Similarity Matrix Numeric Feature Spectral Cluster Numeric Attribute Nominal Attribute 
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 2006

Authors and Affiliations

  • Huilan Luo
    • 1
    • 2
  • Fansheng Kong
    • 1
  • Yixiao Li
    • 1
  1. 1.Artificial Intelligence InstituteZhejiang UniversityHangzhouChina
  2. 2.Institute of Information EngineeringJiangxi University of Science and TechnologyGangzhouChina

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