Research On Data Mining Applied To Network Fault Diagnosis

As the networks have been expanding ever-increasing network complexity, network failures become increasingly acute. Aiming at analyzing the problems in traditional fault diagnosis, this paper presents two kinds of in-depth data mining methods:the association rule mining and classification mining. And they were applied in fault diagnosis to implement intelligent diagnosis of network faults.In the basis of network fault diagnosis which is based on location model-based data mining, together with considering the network fault alarm cluster features, the association rule mining based frequent pattern tree (FP-tree) algorithm is improved, and FP-treeC mining algorithm is proposed to improve the fault diagnosis and location for cluster; according to the network fault features that alarm information increases continuously, association rules incremental update problem about fault diagnosis and location are studied, and an improved timer FP-treeCT incremental updating algorithm for mining association rules is proposed; based on the existing ID3 decision tree mining algorithms, the IID3 algorithm is applied to network fault diagnosis and location. The paper also proposes a plan-based fault restoration model for certain specific types of network fault.