Novel alarm correlation analysis system based on association rules mining in telecommunication networks

2010 ◽  
Vol 180 (16) ◽  
pp. 2960-2978 ◽  
Author(s):  
Tongyan Li ◽  
Xingming Li
Keyword(s):  
Correlation Analysis ◽  
Association Rules ◽  
Telecommunication Networks ◽  
Association Rules Mining ◽  
Alarm Correlation ◽  
Analysis System

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.

Research and Application of a Multidimensional Association Rules Mining Method Based on OLAP

2021 ◽  
Vol 16 (1) ◽  
pp. 75-94
Author(s):  
Hairong Wang ◽  
Pan Huang ◽  
Xu Chen
Keyword(s):  
Association Rules ◽  
Real Data ◽  
Association Mining ◽  
Mining Method ◽  
Association Rules Mining ◽  
Factors Affecting ◽  
Intermediate Data ◽  
On Line ◽  
Analysis System ◽  
Mining Model

As to the problems of low data mining efficiency, less dimensionality, and low accuracy of traditional multidimensional association rules in the university big data environment, an OLAP-based multi-dimensional association rule mining method is proposed, which combines hash function and marked transaction compression technology to solve the problem of excessive or redundant candidate sets in the Apriori algorithm, and uses On Line Analytical Processing to manage the intermediate data in the association mining process , in order to reduce the time overhead caused by repeated calculations. To verify the validity of the proposed method, a learning situation analysis system is constructed in the field of colleges and universities. The multi-dimensional association rules mining method is used to analyze more than 21,000 desensitized real data, in order to mine the key factors affecting students' academic performance. The experimental results show that the proposed multi-dimensional mining model has good mining results and significantly improves the time performance.

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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