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Evaluating several unsupervised class-selection methods

In knowledge discovery from collected databases, one of the firstly arising questions is ”what should be discovered”. Two lines of work can be followed. In the first line, unsupervised learning is performed, usually clustering data, followed by a characterization of the discovered knowledge. In the second line, classifiers are constructed for each highly important feature registered in the database. In the latter approach, the selection of the important features is done by using domain background knowledge provided by human experts in the domain where the data was collected from. Yet, in actual domains, high number of features make difficult to select the important features for which the classifiers should be constructed. In this study, several measures for ranking class-candidate features are proposed and preliminary evaluated on a domain.