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Matrix Visualization and Information Mining

In: COMPSTAT 2004 — Proceedings in Computational Statistics

Author

Listed:
  • Chun-Houh Chen

    (Institute of Statistical Science, Academia Sinica)

  • Hai-Gwo Hwu

    (National Taiwan University Hospital and College of Medicine, National Taiwan University, Department of Psychiatry)

  • Wen-Jung Jang

    (Institute of Statistical Science, Academia Sinica)

  • Chiun-How Kao

    (Institute of Statistical Science, Academia Sinica)

  • Yin-Jing Tien

    (National Central University, Institute of Statistics)

  • ShengLi Tzeng

    (Institute of Statistical Science, Academia Sinica)

  • Han-Ming Wu

    (Institute of Statistical Science, Academia Sinica)

Abstract

Many statistical techniques, particularly multivariate methodologies, focus on extracting information from data and proximity matrices. Rather than rely solely on numerical characteristics, matrix visualization allows one to graphically reveal structure in a matrix.This article reviews the history of matrix visualization, then gives a more detailed description of its general framework, along with some extensions. Possible research directions in matrix visualization and information mining are sketched. Color versions of figures presented in this article, together with software packages, can be obtained from http://gap.stat.sinica.edu.tw/ .

Suggested Citation

  • Chun-Houh Chen & Hai-Gwo Hwu & Wen-Jung Jang & Chiun-How Kao & Yin-Jing Tien & ShengLi Tzeng & Han-Ming Wu, 2004. "Matrix Visualization and Information Mining," Springer Books, in: Jaromir Antoch (ed.), COMPSTAT 2004 — Proceedings in Computational Statistics, pages 85-100, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2656-2_6
    DOI: 10.1007/978-3-7908-2656-2_6
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