- (Makanju et al., 2009) ⇒ Adetokunbo A.O. Makanju, A. Nur Zincir-Heywood, and Evangelos E. Milios. (2009). “Clustering Event Logs Using Iterative Partitioning.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557154
- Categories and Subject Descriptors: I.5.3 Pattern Recognition: Clustering — Algorithms
- General Terms: Algorithms, Experimentation.
The importance of event logs, as a source of information in systems and network management cannot be overemphasized. With the ever increasing size and complexity of today's event logs, the task of analyzing event logs has become cumbersome to carry out manually. For this reason recent research has focused on the automatic analysis of these log files. In this paper, we present IPLoM (Iterative Partitioning Log Mining), a novel algorithm for the mining of clusters from event logs. Through a 3- Step hierarchical partitioning process IPLoM partitions log data into its respective clusters. In its 4th and final stage IPLoM produces cluster descriptions or line formats for each of the clusters produced. Unlike other similar algorithms IPLoM is not based on the Apriori algorithm and it is able to find clusters in data whether or not its instances appear frequently. Evaluations show that IPLoM outperforms the other algorithms statistically significantly, and it is also able to achieve an average F-Measure performance 78% when the closest other algorithm achieves an F-Measure performance of 10%.
|2009 ClusteringEventLogsUsingIterati||Adetokunbo A.O. Makanju|
A. Nur Zincir-Heywood
Evangelos E. Milios
|Clustering Event Logs Using Iterative Partitioning||KDD-2009 Proceedings||10.1145/1557019.1557154||2009|
|Author||Adetokunbo A.O. Makanju +, A. Nur Zincir-Heywood + and Evangelos E. Milios +|
|journal||Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Clustering Event Logs Using Iterative Partitioning +|