Here's a sample session for themushroom dataset1.) mconvert CSVTOCLICK mush.data mush.confusion mush.mapping 23 0 0 >mush.click-> Converts mush.data (the space-separated data) to mush.click. -> mush.confusion is a file where mconvert writes - for each line inmush.data - an integer class label.-> mush.mapping is a file where mconvert saves the mapping betweeninteger classes (which are used by click) and the literal classes in theactual data file.-> 23: total number of columns in mush.data-> 0: Index of the label column (used to derive the class formush.confusion)-> 0: Omit column 0 in the output data file2.) ../../code/kcdriver mush.click 0.1 0.1 click.bench SUB CONFUSION MAPmush.mapping-> Runs CLICK(S) with alpha = 0.1 and minsup = 0.1, benchmark resultsare appended to click.bench, subspace clusters are enabled-> A confusion file is created in click_confusion.txt-> CLICKS uses the mapping information contained in mush.mapping (seestep 1) to output clusters in terms of actual classes (from mush.data)instead of integer class labelsNOTE: just type ./kcdriver for all the options. The code allows you to      mine clusters using alpha*expectation or you can also treat      alpha as just a minsup value (between [0,1]).3.) mconvert CONFUSION mush.confusion click_confusion.txt dummymap.txt-> Output confusion matrix in LaTeX table format. The first file(mush.confusion) contains the actual classes, the second one the classesderived by CLICKS.-> dummymap.txt is an empty file here. You can use is to map the integerclass labels between the two confusion files (e.g. class 1 inmush.confusion corresponds to class 0 in click_confusion, ...)More infos on the two commands can be found in the appendices of thethesis, which is attached in the most recent version.