A LFP-tree based method for association rules mining in telecommunication alarm correlation analysis

2007 ◽  
Vol 14 ◽  
pp. 6-15 ◽  
Author(s):  
Tong-yan LI ◽  
Xing-ming LI
Keyword(s):  
Correlation Analysis ◽  
Association Rules ◽  
Association Rules Mining ◽  
Alarm Correlation

A New Algorithm for the Fuzziness of Alarms in Network Faults Diagnosis

2012 ◽  
Vol 198-199 ◽  
pp. 1539-1544 ◽  
Author(s):  
Pan Liu ◽  
Xing Ming Li ◽  
Jian Wu
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Communication Networks ◽  
Association Rules ◽  
Association Rules Mining ◽  
Fuzzy Association Rules ◽  
Edge Field ◽  
Alarm Correlation ◽  
Faults Diagnosis ◽  
Network Fault

The alarm correlation analysis based on fuzzy association rules mining is the popular and cutting-edge field of the network fault diagnosis research. In the application environment of alarms in communication networks, a new algorithm of the fuzziness of alarms which is called FKMA (Fuzzy K-Means of Alarms algorithm) is proposed .During the process of fuzziness, there are two methods of sorting the center. Simulations are carried out to the comparison of the two methods. The fuzziness of alarms is effectively realized. And fuzzy association rules mining are achieved. The advantages and efficiency of FKMA are demonstrated by experiments.

Communication Network Alarm Correlation Based on the Confidence Fusion Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2237-2240
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Communication Network ◽  
Correlation Analysis ◽  
Association Rules ◽  
Traditional Method ◽  
Sliding Window ◽  
Experimental Results ◽  
Fusion Algorithm ◽  
Alarm Correlation ◽  
Calculation Process ◽  
Calculation Results

In this article we achieve SDH communication network correlation analysis by the confidence fusion algorithm. We take the effect of the alarm topology relationship on the alarm timing relationship into the calculation process of confidence fusion algorithm .Thus it can get alarm correlation results more in line with objective facts. According to the correlation calculation results it forms the alarm correlation transactions, then applying the alarm correlation transactions to the alarm association rules mining. We compare the mining results with the traditional method based on sliding window. The experimental results show that the mining results are more effective after introducing the confidence fusion algorithm.

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