Text Categorization Based On Fuzzy Logic And Neural Networks

Because of the rapid growth of the Internet, an avalanche of information begins to exist in such form that the computers can read, and its quantity is still in sharp growth. For the effectively managing and using of such massive distributed information, the technologies of information retrieval and the text mining based on contents have received the attention. The text categorization (or classification) is the foundation of information retrieval and the text mining, what this technology needs to do is that under the classified system, it tries to assign its respective category according to the text content. At present days, the text categorization has already made some substantial progress, such regular text categorization approach assigns the given text to map the text category, but it is not ideal when we need to handle the 1 to n text problems.In this paper, fuzzy logic and neural networks are combined for text categorization. The text of the training methods will feature eigenvector fuzzy, at the same time, each category of fuzzy center vector characteristics. Fuzzy the text of the vector and fuzzy category center received the text of each type of distance, through a membership function from the definition by membership. This paper uses fuzzy eigenvector of the text and the text of each category of membership vector to train neural networks. The text will be tested after eigenvector fuzzy neural networks can get through the text relative to each category of membership, each belonging to the text adopted by the membership categories can be judged on the text category.At the end of this paper we specify the method based on the fuzzy logic and neural network model, explain the correlated data structure and the algorithm, and then test this model.