Advances in Neural Networks - ISNN 2008

Volume 5264 of the series Lecture Notes in Computer Science pp 30-39

Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation

  • Yan LiuAffiliated withMotorola Labs
  • , Jing ZhangAffiliated withMotorola Labs
  • , Xin MengAffiliated withMotorola Inc.
  • , John StrassnerAffiliated withMotorola Labs

* Final gross prices may vary according to local VAT.

Get Access


Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.


Alarm correlation Sequential proximity Clustering Metrics