Matrix Visualization and Information Mining

Chun-Houh Chen (), Hai-Gwo Hwu, Wen-Jung Jang, Chiun-How Kao, Yin-Jing Tien, ShengLi Tzeng and Han-Ming Wu
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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

A chapter in COMPSTAT 2004 — Proceedings in Computational Statistics, 2004, pp 85-100 from Springer

Abstract: 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/ .

Keywords: Dimension free visualization; effect-ordered data display; generalized association plots; heat-map; matrix map; relativity of statistical graph; re-orderable matrix; sufficient statistical graph (search for similar items in EconPapers)
Date: 2004
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DOI: 10.1007/978-3-7908-2656-2_6

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