Classification Algorithm Based On Hybrid Intelligent Systems, Data Mining

With the deepening of the application of database, the size of database expands quickly, people need to analyse these data and find the worthy informations. But the database management systems do not provide available tools to analyse and use these data, so data mining appears and becomes the hotspot. Among this Classifacation is payed much attention for is's widely use by people. This thesis researchs the classification based on Hybrid Intelligent System.The author summarizes the researching actuality and the production of application, then analyses the standard theories . Based on the psychology's and aggregative model's theory analysis, the author proposes a new HIS—R-FC-DENN according to Rough Set, Clustering theory , Fuzzy Logic , Genetic Algorithm and Artificial Neural Network.First, R-FC-DENN uses the Rough Set to reduced the data. And then it cluster the data used the Clustering theory. After that it uses different and improved ANN to train. Subsequently the data trained is fabricated by fuzzy power. Last the data is trained by another improved ANN and the whole process of training is completed.Last the author devises and achieves the R-FC-DENN according to every functional module. Then the HIS is fabricated under the surroundings of Matlab6.5.Thus the RFCDENNTool is developed. Subsequently the author uses the UCI's databases to prove the utility of the new HIS—R-FC-DENN and gets the satisfied answers.