Confidence rule automatic generation method based on fuzzy clustering

Abstract

The invention discloses a confidence rule automatic generation method based on fuzzy clustering, and belongs to the technical field of data mining. In order to solve a problem that time and labor areconsumed when a confidence rule base is established completely depending on artificial experience, on the basis of an extended confidence rule base method, firstly, fuzzy clustering is carried out onsample data, and a fuzzy clustering center of a sample and the sample with the clustering center as the circle center and the distance between the sample and the clustering center being a certain setvalue serve as basic data for generating an extended confidence rule base; and calculating a premise attribute weight and a rule weight according to the variable association relationship and the sample membership matrix. The objective of the invention is to solve the problems of difficult selection of sample data and unsuitability for large sample data modeling in the extended confidence rule basemethod. According to the method, the dependency degree of experience setting premise attribute weights and initial rule weights in the existing extended confidence rule base method on expert experience is reduced. By adopting the method, the confidence rule is extracted from the actual production data, and compared with manually set initial confidence rules, the generation efficiency and the reasoning precision of the confidence rule base are improved.

Description

technical field [0001] The invention relates to a method for automatically generating confidence rules based on fuzzy clustering, which belongs to the technical field of data mining. Background technique [0002] The automatic transformation of data into knowledge is a necessary technology for the development of intelligent manufacturing. The confidence rule base method is a knowledge-based method developed on the basis of D-S evidence theory, decision theory, fuzzy theory and the traditional IF-THEN rule base. The ability to model. Compared with traditional IF-THEN production rules, confidence rules add weight parameters such as rule weight, premise attribute weight, and result confidence, which not only expands the scope of rule description, but also fully combines the randomness and fuzziness of knowledge, providing A knowledge expression method that is closer to reality and can contain more information. The confidence rule base method can utilize quantitative producti...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the selection of sample data in the extended confidence rule base method is difficult, which makes it unsuitable for large sample data modeling; and to reduce the empirically set premise attribute weight and initial rules in the existing extended confidence rule base method Dependence of weights on expert experience

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