Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation

Authors:
Yan Liu;Jing Zhang;Xin Meng;John Strassner
Affiliations:
Motorola Labs, Schaumburg, USA 60193;Motorola Labs, Schaumburg, USA 60193;Motorola Inc., Beijing, China;Motorola Labs, Schaumburg, USA 60193
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

Citing 6
Cited 0

A Framework for Event Correlation in Communication Systems

MMNS '01 Proceedings of the 4th IFIP/IEEE International Conference on Management of Multimedia Networks and Services: Management of Multimedia on the Internet
A Comparison of Association Rule Discovery and Bayesian Network Causal Inference Algorithms to Discover Relationships in Discrete Data

AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
On Binary Similarity Measures for Handwritten Character Recognition

ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
FECk: A New Efficient Clustering Algorithm for the Events Correlation Problem in Telecommunication Networks

FGCN '07 Proceedings of the Future Generation Communication and Networking - Volume 01
Survey of clustering algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.