GAP (generalized association plots): a graphical environment for matrix visualization and cluster analysis. 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.

References in zbMATH (referenced in 18 articles , 2 standard articles )

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  1. Ghandehari, Mahya; Janssen, Jeannette: An optimization parameter for seriation of noisy data (2019)
  2. Hahsler, Michael: An experimental comparison of seriation methods for one-mode two-way data (2017)
  3. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  4. Shiu, Shang-Ying; Chen, Ting-Li: On the strengths of the self-updating process clustering algorithm (2016)
  5. Vigneron, V.; Chen, H.: A multi-scale seriation algorithm for clustering sparse imbalanced data: application to spike sorting (2016)
  6. Gosnell, Denise K.; Berry, Michael W.: Using semidiscrete decomposition and vector space models to identify users of social networks. (2015)
  7. Kao, Chiun-How; Nakano, Junji; Shieh, Sheau-Hue; Tien, Yin-Jing; Wu, Han-Ming; Yang, Chuan-kai; Chen, Chun-houh: Exploratory data analysis of interval-valued symbolic data with matrix visualization (2014)
  8. Antoch, Jaromír; Prchal, Luboš; Sarda, Pascal: Combining association measures for collocation extraction using clustering of receiver operating characteristic curves (2013)
  9. Wittek, Peter: Two-way incremental seriation in the temporal domain with three-dimensional visualization: making sense of evolving high-dimensional datasets (2013)
  10. Yao, Wei-Ting; Wu, Han-Ming: Isometric sliced inverse regression for nonlinear manifold learning (2013)
  11. Chepoi, Victor; Seston, Morgan: Seriation in the presence of errors: a factor 16 approximation algorithm for (l_\infty)-fitting Robinson structures to distances (2011)
  12. Gatu, Cristian (ed.); McCullough, B. D. (ed.): Editorial: Second special issue on statistical algorithms and software (2010)
  13. Liiv, Innar: Seriation and matrix reordering methods: an historical overview (2010)
  14. Rajaram, Satwik; Oono, Yoshi: Neatmap - non-clustering heat map alternatives in R (2010) ioport
  15. Wu, Han-Ming; Tien, Yin-Jing; Chen, Chun-Houh: GAP: a graphical environment for matrix visualization and cluster analysis (2010)
  16. Peltonen, Jaakko; Venna, Jarkko; Kaski, Samuel: Visualizations for assessing convergence and mixing of Markov chain Monte Carlo simulations (2009)
  17. Chang, Shun-Chuan; Chen, Chun-Houh; Chi, Yueh-Yun; Ouyoung, Chih-Wen: Relativity and resolution for high dimensional information visualization with generalized association plots (GAP) (2002)
  18. Chen, Chun-Houh: Generalized association plots: Information visualization via iteratively generated correlation matrices (2002)