DCSA: Using Density-Based Clustering and Sequential Association Analysis to Predict Alarms in Telecommunication Networks

Traditional alarm prediction in telecommunication networks mainly relies on expert knowledge. However, with the increasing complexity of telecommunication network, the traditional methods may not work well. It's necessary to study new automatic association rules extraction methods. In this pape...

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Veröffentlicht in:2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS) S. 1 - 8
Hauptverfasser: Lin, Peng, Ye, Kejiang, Chen, Ming, Xu, Cheng-Zhong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2019
Schlagworte:
Alarm correlation analysis
alarm prediction
Association rule learning
Clustering algorithms
Communications technology
Correlation
DBSCAN
Distributed databases
Filtering
Knowledge engineering
Prediction algorithms
Redundancy
sequential association analysis
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Zusammenfassung:Traditional alarm prediction in telecommunication networks mainly relies on expert knowledge. However, with the increasing complexity of telecommunication network, the traditional methods may not work well. It's necessary to study new automatic association rules extraction methods. In this paper, we proposed a method called DCSA (Density-based Clustering and Sequential Association Analysis) for alarm association rules mining. We use time density-based clustering and FP-Growth algorithm to mine the association rules in alarm data, which overcomes the drawbacks of sliding windows method. In addition, we design a sequential rules filtering module to eliminate the items that do not meet the sequential conditions in the original rules. Experiments on 7.5 million real alarm items from a telecommunication company of China show the sequential rules filtering module can greatly reduce the redundancy of association rules. We also demonstrate that the proposed DCSA method could effectively predict the occurrence of alarms.
DOI:10.1109/ICPADS47876.2019.00010