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

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.

2011 ◽  
Vol 13 (6) ◽  
pp. 809-819 ◽  
Author(s):  
S. Vinodh ◽  
K. Eazhil Selvan ◽  
N. Hari Prakash

2019 ◽  
Vol 50 (2) ◽  
pp. 448-467 ◽  
Author(s):  
Zhongjie Zhang ◽  
Jian Huang ◽  
Jianguo Hao ◽  
Jianxing Gong ◽  
Hao Chen

2012 ◽  
Vol 12 (8) ◽  
pp. 2114-2122 ◽  
Author(s):  
Hung-Pin Chiu ◽  
Yi-Tsung Tang ◽  
Kun-Lin Hsieh

2014 ◽  
Vol 998-999 ◽  
pp. 842-845 ◽  
Author(s):  
Jia Mei Guo ◽  
Yin Xiang Pei

Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.


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