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GAP: A graphical environment for matrix visualization and cluster analysis

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  • Wu, Han-Ming
  • Tien, Yin-Jing
  • Chen, Chun-houh

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.

Suggested Citation

  • Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:767-778
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    References listed on IDEAS

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    1. Hahsler, Michael & Hornik, Kurt & Buchta, Christian, 2008. "Getting Things in Order: An Introduction to the R Package seriation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i03).
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    3. Friendly M., 2002. "Corrgrams: Exploratory Displays for Correlation Matrices," The American Statistician, American Statistical Association, vol. 56, pages 316-324, November.
    4. Chavent, Marie & Lechevallier, Yves & Briant, Olivier, 2007. "DIVCLUS-T: A monothetic divisive hierarchical clustering method," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 687-701, October.
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    Cited by:

    1. Wittek, Peter, 2013. "Two-way incremental seriation in the temporal domain with three-dimensional visualization: Making sense of evolving high-dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 193-201.
    2. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Liu, Kailiang & Xu, Zhitong & Chen, Chun-houh & Nakano, Junji & Honda, Keisuke, 2023. "Article’s scientific prestige: Measuring the impact of individual articles in the web of science," Journal of Informetrics, Elsevier, vol. 17(1).
    3. Bohdan B Khomtchouk & James R Hennessy & Claes Wahlestedt, 2017. "shinyheatmap: Ultra fast low memory heatmap web interface for big data genomics," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-9, May.

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