Fuzzification and reduction of information-theoretic rule sets

If-then rules are one of the most common forms of knowledge discovered by data mining methods. The number and the length of extracted rules tend to increase with the size of a database, making the rulesets less interpretable and useful. Existing methods of extracting fuzzy rules from numerical data improve the interpretability aspect, but the dimensionality of fuzzy rulesets remains high. In this paper, we present a new methodology for reducing the dimensionality of rulesets discovered in data. Our method builds upon the information-theoretic fuzzy approach to knowledge discovery. We start with constructing an information-theoretic network from a data table and extracting a set of association rules based on the network connections. The set of information-theoretic rules is fuzzified and significantly reduced by using the principles of the Computational Theory of Perception (CTP). We demonstrate the method on a real-world database from semiconductor industry.