Abstract
In this paper we propose a method to pre-process a telecommunication alarm log with the aim of discovering more accurately frequent patterns. In a first step, the alarm types which present the same temporal behavior are clustered with a self organizing map. Then, the log areas which are rich in alarms of the clusters are searched. The sublogs are built based on the selected areas. We will show the efficiency of our preprocessing method through experiments on an actual alarm log from an ATM network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Möller, M., Tretter, S., Fink, B.: Intelligent filtering in network-management systems. In: Proceedings of the 4th International Symposium on Integrated Network Management, pp. 304–315 (1995)
Nygate, Y.A.: Event correlation using rule and object base techniques. In: Proceedings of the 4th International Symposium on Integrated Network Management, pp. 279–289 (1995)
Dousson, C.: Extending and Unifying Chronicle Representation with Event Counters. In: Proceedings of the 15th ECAI, pp. 257–261 (2002)
Jakobson, G., Weissman, M.: Real-time telecommunication network management: extending event correlation with temporal constraints. In: Proceedings of the 4th International Symposium on Integrated Network Management, pp. 290–301 (1995)
Dousson, C., Vu Duong, T.: Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In: Proceedings of the 16th IJCAI, pp. 620–626 (1999)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)
Oja, E., Kaski, S.: Kohonen maps. Elsevier, Amsterdam (1999)
Vesanto, J., Alhoniemi, E.: Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab, Report A57 Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fessant, F., Clérot, F., Dousson, C. (2004). Mining of an Alarm Log to Improve the Discovery of Frequent Patterns. In: Perner, P. (eds) Advances in Data Mining. ICDM 2004. Lecture Notes in Computer Science(), vol 3275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30185-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-30185-1_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24054-9
Online ISBN: 978-3-540-30185-1
eBook Packages: Computer ScienceComputer Science (R0)