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Title: cGAPdb: A matrix visualization database for categorical data sets Authors:  Chun-houh Chen - Academia Sinica (Taiwan) [presenting]
Shao-An Chen - Tamkang University (Taiwan)
Chiun-How Kao - Tamkang University (Taiwan)
Sheau-Hue Shieh - National Taipei University (Taiwan)
Han-Ming Wu - National Chengchi University (Taiwan)
Abstract: cGAPdb is a graphical database for categorical data sets for public use. The major type of visualization provided in this database is matrix visualization with the cGAP (Categorical Generalized Association Plots) environment. Most of the categorical data sets from the UCI Machine Learning Repository are included in this graphical database. All elements of a cGAP display such as (homals analysis, data matrix, proximity matrix for variables and samples, seriation method, etc.) are provided for each data set for users to browse and download. Additional categorical data sets other than those from the UCI Repository have also been collected in cGAPdb. A cGAP working place is available in cGAPdb for users to upload their own data sets for creating cGAP matrix visualization displays.