Applications of fuzzy logic functions to knowledge discovery in databases

Authors:
Noboru Takagi;Hiroaki Kikuchi;Masao Mukaidono
Affiliations:
Department of Electronics and Informatics, Toyama Prefectural University, Toyama, Japan;Department of Information Media Technology, Tokai University, Kanagawa, Japan;Department of Computer Science, Meiji University, Kanagawa, Japan
Venue:
Transactions on Rough Sets II
Year:
2005

Citing 6
Cited 1

Discrete mathematics (2nd ed.)

Discrete mathematics (2nd ed.)
Advances in knowledge discovery and data mining

Advances in knowledge discovery and data mining
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
On the Properties and Applications of Fuzzy-Valued Switching Functions

IEEE Transactions on Computers
On Minimization of Fuzzy Functions

IEEE Transactions on Computers
Selecting fuzzy if-then rules for classification problems using genetic algorithms

IEEE Transactions on Fuzzy Systems

Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

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.