Research Of Data Mining Techniques Based On Fuzzy System And Genetic Algorithm

Data mining is a set of methods efficient in analyzing large data sets, however for the inherent uncertainty and complex of data and the attributes, some methods show their inability in some cases. Soft computing is good at dealing with such dilemma, therefore it is valuable to study data mining techniques in the frame of soft computing. In this paper, the combination of fuzzy model and genetic algorithm is presented to mine two kinds of knowledge: prediction and classification. The following points are concentrated in this paper:1) The background of data mining's emergence, the definition and the process of data mining, the mined knowledge and corresponding methods are presented. 2) As the basis of following study, the theory on fuzzy model and genetic algorithm is briefly introduced.3) For prediction, a method is proposed which is based on the model of fuzzy system and optimized by improved genetic algorithm. As an instance, the historical data of some forecast station is analyzed using the method to predict the amount of precipitation, the contrast with the result from multivariable regression shows that the method has better performance.4) For classification, a classifier composed of fuzzy rules is given, the fuzzy rules are simple consisting of an attribute, the corresponding class and an influence factor. Genetic algorithm is used to find the best subset of fuzzy rules. In the instance, the classical data set of Iris is studied, which is studied widely in machine learning domain, as the result, a subset with less rules and more accuracy is obtained...