GAP: A graphical environment for matrix visualization and cluster analysis

Authors:
Han-Ming Wu;Yin-Jing Tien;Chun-houh Chen
Affiliations:
Department of Mathematics, Tamkang University, Taipei County 25137, Taiwan;Institute of Statistics, National Central University, Taoyuan 32001, Taiwan;Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan
Venue:
Computational Statistics & Data Analysis
Year:
2010

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Abstract

GAP is a Java-designed exploratory data analysis (EDA) software for matrix visualization (MV) and clustering of high-dimensional data sets. It provides direct visual perception for exploring structures of a given data matrix and its corresponding proximity matrices, for variables and subjects. Various matrix permutation algorithms and clustering methods with validation indices are implemented for extracting embedded information. GAP has a friendly graphical user interface for easy handling of data and proximity matrices. It is more powerful and effective than conventional graphical methods when dimension reduction techniques fail or when data is of ordinal, binary, and nominal type.