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Matrix Visualization

  • Han-Ming Wu
  • ShengLi Tzeng
  • Chun-houh Chen
Part of the Springer Handbooks Comp.Statistics book series (SHCS)

Abstract

The graphical exploration of quantitative/qualitative data is an initial but essential step inmodern statistical data analysis.Matrix visualization (Chen, 2002; Chen et al., 2004) is a graphical technique that can simultaneously explore the associations between thousands of subjects, variables, and their interactions, without needing to first reduce the dimensions of the data. Matrix visualization involves permuting the rows and columns of the raw data matrix using suitable seriation (reordering) algorithms, together with the corresponding proximity matrices.The permuted raw data matrix and two proximity matrices are then displayed as matrix maps via suitable color spectra, and the subject clusters, variable groups, and interactions embedded in the dataset can be extracted visually.

Keywords

Exploratory Data Analysis Proximity Measure Proximity Matrix Hierarchical Cluster Tree Binary Data Matrix 
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

  • Han-Ming Wu
    • 1
  • ShengLi Tzeng
    • 1
  • Chun-houh Chen
    • 1
  1. 1.Institute of Statistical ScienceAcademia SinicaNangkangChina

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