Abstract
This chapter is a summary of knowledge discovery algorithms that take an input of training examples of target knowledge, and output a fuzzy logic formula that best fits the training examples. The execution is done in three steps; first, the given mapping is divided into some Q-equivalent classes; second, the distances between the mapping and each local fuzzy logic function are calculated by a simplified logic formula; and last, the shortest distance is obtained by a modified graph-theoretic algorithm. After a fundamental algorithm for fitting is provided, fuzzy logic functions are applied to a more practical example of classification problem, in which expressiveness of fuzzy logic functions is examined for a well-known machine learning database.
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Takagi, N., Kikuchi, H., Mukaidono, M. (2004). Applications of Fuzzy Logic Functions to Knowledge Discovery in Databases. In: Peters, J.F., Skowron, A., Dubois, D., Grzymała-Busse, J.W., Inuiguchi, M., Polkowski, L. (eds) Transactions on Rough Sets II. Lecture Notes in Computer Science, vol 3135. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27778-1_7
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