Study On Attributions Selection And Rule Extraction Of Data Mining For Classification Based On Neural Networks

Data mining is a new technolody that is used to extract useful information and knowledge from large databases. Classification is an important task of data mining. Facing the massive volume and high dimensional data how to build effective and scalable algorithm for data mining is one of research directions of data minming.Attributions selecton and rule extraction are the most important topics in data mining. And neural network is one of the important mining tools.However, traditional neural network methods require training all attributions, which causes the deficiencies of excessive large size of network and low efficiency. This paper presents some new methods of attributions selection and rule extraction in data mining using neural network and fuzzy logic, which are listed as follows.A method of RBF neural network attributions selection based on data input-output correlation ranking is presented in this paper. This method ranks the attributions in the order of data input-output correlation and selects the attributions using RBF neural network based on the attributions ranking. It avoids the deficiency of traditional neural network methods needing to train all attributions, which greatly improves the efficiency of attributions selection.Another method of RBF neural networks attributions selection based on a separability criterion ranking is presented in this paper. It ranks the attributions using the separability criterion for the attributions and selectes the attributions using RBF neural network based on the attributions ranking.A new rule extraction methed is presented in this paper. In this method, a part of important attributions are first selected from prime attributions using the above methods. After fuzziness of these important attributions, the rule extraction is carried out using a probable neural network. This method greatly reduces the neural network's size through the attributions selection, and so largely improves the efficincy of the rule extraction compared with existing similar methods. At the same time, the knowledge extracted can be easily understanded and the rule acuuracy is also improved.