Topic
Fuzzy associative matrix
About: Fuzzy associative matrix is a research topic. Over the lifetime, 8027 publications have been published within this topic receiving 194790 citations.
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01 Oct 1996
TL;DR: Fuzzy rules and defuzzification: rules based on experience learning from examples decision tree approach neural network approach minimization of fuzzy rulesdefuzzification and optimization applications concluding remarks.
Abstract: Introduction: Fuzzy sets probability and fuzziness fuzzy models Membership functions: heuristic selections clustering approaches adjustment and toning applications concluding remarks Fuzzy clustering: clustering and fuzzy partition fuzzy c-means algorithm fuzzy cohonen clustering networks cluster validity and optimal fuzzy clustering applications concluding remarks Fuzzy rules and defuzzification: rules based on experience learning from examples decision tree approach neural network approach minimization of fuzzy rules defuzzification and optimization applications concluding remarks Fuzzy classifiers: fuzzy nearest neighbour classifier fuzzy multilayer perceptron fuzy decision trees fuzzy string matching applications concluding remarks Combined clasifications: introduction voting schemes maximum poteriori probability Dempster-Shafer evidence theory trained perceptron neural networks applications concluding remarks
528 citations
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TL;DR: This paper will give a characteristic of a field by a fuzzy ideal, as fuzzy invariant subgroups, fuzzy ideals, and to prove some fundamental properties of fuzzy algebra.
526 citations
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01 Mar 1998TL;DR: This paper introduces the fuzzy association rules of the form, 'If X is A then Y is B', to deal with quantitative attributes, using the fuzzy set concept, to find association rules more understandable to human.
Abstract: Data mining is the discovery of previously unknown, potentially useful and hidden knowledge in databases. In this paper, we concentrate on the discovery of association rules. Many algorithms have been proposed to find association rules in databases with binary attributes. We introduce the fuzzy association rules of the form, 'If X is A then Y is B', to deal with quantitative attributes. X, Y are set of attributes and A, B are fuzzy sets which describe X and Y respectively. Using the fuzzy set concept, the discovered rules are more understandable to human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries.
524 citations
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TL;DR: An algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques is proposed.
524 citations
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TL;DR: This paper introduces the concept of distributed representation of fuzzy rules and applies it to classification problems, and proposes a fuzzy inference method using the generated fuzzy rules.
513 citations