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A Hybrid Visualization-Induced Self-Organizing Map for Multi Dimensional Reduction and Data Visualization

  • Chee Siong Teh
  • Ming Leong Yii
  • Chwen Jen Chen
  • Zahan Tapan Sarwar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

Self-Organizing Map (SOM), being a prominent unsupervised learning algorithm, is often used for multivariate data visualization. However, SOM only preserves inter-neurons distances in the input space and not in the output space due to the rigid grid used in SOM. Visualization-induced Self-Organizing Map (ViSOM) has been proposed as a visualization-wise improved variation of the popular unsupervised SOM. However ViSOM suffers from dead neuron problem as a huge number of neurons fall outside of the data region due to the regularization effect, even when the regularization control parameter is properly chosen. In this paper, a hybrid ViSOM that employs a modified Adaptive Coordinates (AC) technique is proposed for data visualization. Empirical studies of the hybrid technique yield promising topology preserved visualizations and data structure exploration for synthetic as well as benchmarking datasets.

Keywords

Self-Organizing Map Visualization induced Self-Organizing Map Adaptive Coordinates Multivariate Data Visualization Multi-dimension Reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chee Siong Teh
    • 1
  • Ming Leong Yii
    • 1
  • Chwen Jen Chen
    • 1
  • Zahan Tapan Sarwar
    • 1
  1. 1.Faculty of Cognitive Sciences and Human DevelopmentUniversiti Malaysia Sarawak (UNIMAS)Kota SamarahanMalaysia

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