| The development of the techniques
for the matrix visualization is the main research area in Chen's
lab. The proposed method is called generalized association plot
(GAP). It is a dimension-free information visualization
environment for exploring the association of subjects/variables
embedded in the data. The framework of GAP consists of four
components: (1) presentation of raw data matrix and selection of
proximity matrices, (2) seriation of proximity matrices and raw
matrix, (3) partitions of permuted matrix maps, and (4) sufficient
statistical graph.
Most of the data for statistical
analysis are the object for matrix visualization. For example, we
have developed a matrix visualization approach for categorical
data, and have applied it on cartographic display. Although the
matrix visualization is not a new research area, it still has many
research topics and technical applications. By now, the research
achievement in Chen’s lab has reached the world’s leading
position. Currently, we are developing new methods for various
types of data and looking for the new applications actively.
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