Quality Assessment and Uncertainty Handling in Data Mining Process

The KDD process aims at the discovery and extraction of “useful” knowledge (such as interesting patterns, classification, rules etc) from large data repositories. A widely recognized requirement is that the patterns discovered must be valid and ultimately comprehensible (i.e., to be easily understood by analysts). Another requirement that is under-addressed in KDD process is the reveal and the handling of uncertainty in the main data mining processes of clustering, classification and association rules extraction.