COSA with R
COSA(tm) is an implementation of the COSA method for clustering objects (observations) on subsets of attributes (variables) described in Clustering objects on subsets of attributes (pdf).
The files provided here implement a rudimentary interface for using COSA(tm) with the R statistical package. R is freely available from The R Project for Statistical Computing. This R/COSA interface runs on PC compatible computers with Windows NT/2000/XP or Linux. This present version of the R/COSA interface will not function after December 29, 2017. Future versions will be available after that date.
It is assumed that R has already been installed on the computer. The first step is to create a (target) directory in which to store the downloaded files. This directory should be used only for the R/COSA installation and contain no other files or sub-directories. Its full path name must contain NO imbedded blanks. For Windows, this directory must reside on the same logical disk where R is installed.
Windows NT/2000/XT/7
Transfer the files install.bat, ascii.bat, extract.exe, r_cosa.exe, readfile.exe to the target directory with these same names.
Execute install.bat. (double click file icon, or Start -> Run -> directory\install, where directory is the full path name of the target directory to which the above files were downloaded. This will create a file readme.txt as well as a number of other files in the target directory.
Open readme.txt with Notepad (Start -> Programs -> Accessories -> Notepad), or another text editor.
Follow the instructions in readme.txt to complete the installation.
Linux
Transfer the files ascii.bat, extract.exe, r_cosa.exe, readfile.exe to the target directory with these same names.
Execute the following commands at the Linux command prompt in the target directory:
Follow the instructions in readme.txt to complete the installation.
Complete documentation for each R/COSA procedure is provided with associated help files, as described in the readme.txt file provided with the installation. Some familiarity with the above paper helpful when experimenting with some of the more advanced features and options.
www@stat.stanford.edu