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In order to improve the efficiency of database management and intelligence of database record classification, a classification method of network database record based on fuzzy theory is proposed in this paper. Firstly, an automatic classification frame of database is constructed, and then standard record model and special data record and new record model on fuzzy set are given. By calculating the...
The use of Bayesian Network Classifiers (BCs) combined with the Fuzzy rule model to explain the learned BCs have been previously presented as the BayesFuzzy approach. This paper follows along BayesFuzzy lines of investigation aiming at improving the comprehensibility of a BC model and enhancing BayesFuzzy results by combining new pruning methods. In order to improve BayesFuzzy performance, in addition...
In an increasingly competitive market, the management of client relationship is becoming a key point for a enterprise to get a success in the competition, client subdivision is a foundation for the enterprise to make a precise marketing strategy and a successful management of client group, based on the development of data mining technology, a fuzzy-C-means(FCM) algorithm model is founded to do the...
Feature selection has recently been the subject of intensive research in data mining, especially for datasets with a large number of descriptive attributes such as feature selection in customer relationship management (CRM). In this paper, FRI algorithm which has some deficiencies in feature selection of market segments groups is improved. A new FC-based GMDH model is built. It has the advantage of...
This paper presents G-REX, a versatile data mining framework based on genetic programming. What differs G-REX from other GP frameworks is that it doesn't strive to be a general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a multitude of predefined classification and regression models. Examples...
In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy modelpsilas parameters. Based on this methodology, the generated fuzzy model...
Due to the learning problem on skewed distribution datasets, which tend to produce high accuracy over the majority class but poor predictive accuracy over the minority class by traditional machine learning algorithms, fuzzy information granulation based knowledge discovery and decision support model called FIG mode is proposed in this paper to improve classification performance and make effective...
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