Uncertainty handling in the data mining process with fuzzy logic

The KDD process aims at searching for interesting instances of patterns in data sets. It is widely accepted that the patterns must be comprehensible. One of the aspects that are under-addressed in the KDD process is the handling of uncertainty in the process of clustering, classification and association rules extraction. In this paper we present a classification framework for relational databases so as to support uncertainty in terms of natural language queries and assessments. More specifically, we present a classification scheme of non-categorical attributes into lexically defined categories based on fuzzy logic and provides decision support facilities based on related information measures.