Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation

  • Yan Liu
  • Jing Zhang
  • Xin Meng
  • John Strassner
Conference paper

DOI: 10.1007/978-3-540-87734-9_4

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5264)
Cite this paper as:
Liu Y., Zhang J., Meng X., Strassner J. (2008) Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation. In: Sun F., Zhang J., Tan Y., Cao J., Yu W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

Alarm correlation Sequential proximity Clustering Metrics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yan Liu
    • 1
  • Jing Zhang
    • 1
  • Xin Meng
    • 2
  • John Strassner
    • 1
  1. 1.Motorola LabsSchaumburgUSA
  2. 2.Motorola Inc.BeijingChina

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