| With the rapid development of information technology, the use of mobile communication tools to infiltrate every aspect of daily life and people is increasingly demanding for the quality of mobile communication network service. As the user base is increasing, mobile communications networks is also increasing pressure.In the traditional artificial methods about communication network fault detection, network managers need to do a lot of work and the process is tedious but not satisfied with the quality test.It is an effective means of fault management using software to manage the network. The mobile network fault detection system base on data mining not only reduces the workload of network managers and the test results more accurate, but also can reduce network management costs.This paper presents a data mining knowledge discovery on the network faults detection, network faults alarms and network fault management, which is depending on the present network developing characters. The method uses clustering method of data mining to process the communication network data, and then uses data mining-related knowledge to analysis the result, with the relevant communication features of network failures and get the causes of the network fault.This paper focuses on the method about data mining knowledge in the mobile communication network data processing. By using the knowledge of data mining in large data communication network, the method extracts useful data to determine fault.In this paper, we us the K-Medoids algorithm to process the date of the communication network data and then compare the result, use the knowledge of mobile communication to analysis the result for getting the network fault. According to the specific circumstances of network fault, we express this usin computer knowledge and stored in the knowledge base.Finally, the paper also describes some knowledge about the expert system representation.Using a reasonable method of knowledge representation can improve the performance of expert systems... |
