Visualization Architecture Based on SOM for Two-Class Sequential Data

  • Ken-ichi Fukui
  • Kazumi Saito
  • Masahiro Kimura
  • Masayuki Numao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4252)


In this paper, we propose a visualization architecture that constructs a map suggesting clusters in sequence that involve classification utilizing the class label information for the display method of the map. This architecture is based on Self-Organizing Maps (SOM) that are to create clusters and to arrange the similar clusters near within the low dimensional map. This proposed method consists of three steps, firstly the winner neuron trajectories are obtained by SOM, secondly, connectivity weights are obtained by a single layer perceptron based on the winner neuron trajectories, finally, the map is visualized by reversing the obtained weights into the map. In the experiments using time series of real-world medical data, we evaluate the visualization and classification performance by comparing the display method by the number of sample ratio for classes belonging to each cluster.


Reference Vector Display Method Winner Neuron Connectivity Weight Inspection Interval 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ken-ichi Fukui
    • 1
  • Kazumi Saito
    • 2
  • Masahiro Kimura
    • 3
  • Masayuki Numao
    • 1
  1. 1.The Institute of Scientific and Industrial ResearchOsaka UniversityJapan
  2. 2.NTT Communication Science LaboratoriesJapan
  3. 3.Department of Electronics and InformaticsRyukoku UniversityJapan

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