Information-Theoretic Fuzzy Approach to Knowledge Discovery in Databases

We suggest a novel, unified approach to automating the entire process of Knowledge Discovery in Databases (KDD). The approach builds upon Shannon’s Information Theory, statistical estimation methods, and Fuzzy Logic. The KDD stages to be automated include: dimensionality reduction, discovering informative rules (patterns), predicting values of unknown attributes, and cleaning a dataset from lowly reliable data.

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