Application and Research of Data Mining Technology in Communication Network Environment

Abstract

The abnormal data of communication networks are complex and diverse, it is difficult to recognize and mine the abnormal data accurately and effectively with traditional methods. In order to improve the recognition accuracy of communication network, a data mining algorithms based on the method of communication network abnormal data recognition is presented. Firstly, the communication network data samples are analyzed for fuzzy c-means clustering in order to obtain the degree of membership matrix. Secondly, the training samples of communication network abnormal data mining are selected according to the membership. At last, the training samples are put into the least square support vector machine learning, which establish the model of abnormal data identification in communication network. The performance of the algorithm was tested by simulation tests, and the results show that, the abnormal data recognition efficiency and accuracy in this paper was improved much more than the traditional identification methods.