Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery

  • Nicolas Durand
  • Bruno Crémilleux
  • Einoshin Suzuki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method VisuMClust has been confirmed with experiments using artificial and real-world data sets.


Association Rule Knowledge Discovery Cluster Result Baseline Method Candidate Group 
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

  • Nicolas Durand
    • 1
  • Bruno Crémilleux
    • 1
  • Einoshin Suzuki
    • 2
  1. 1.GREYC CNRS UMR 6072University of Caen Basse-NormandieFrance
  2. 2.Department of Informatics, ISEEKyushu UniversityFukuokaJapan

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