Clustering event logs using iterative partitioning Export

@inproceedings{citeulike:5044004, abstract = {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\%.}, address = {New York, NY, USA}, author = {Makanju, Adetokunbo A. O. and Nur and Milios, Evangelos E.}, booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {5044004}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1557019.1557154}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1557019.1557154}, doi = {10.1145/1557019.1557154}, isbn = {978-1-60558-495-9}, keywords = {clicks, clustering}, location = {Paris, France}, pages = {1255--1264}, posted-at = {2009-07-03 09:27:38}, priority = {2}, publisher = {ACM}, title = {Clustering event logs using iterative partitioning}, url = {http://dx.doi.org/10.1145/1557019.1557154}, year = {2009} }

TY - CONF ID - citeulike:5044004 L3 - citeulike-article-id:5044004 N2 - 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%. CY - Paris, France SN - 978-1-60558-495-9 AD - New York, NY, USA EP - 1264 TI - Clustering event logs using iterative partitioning PB - ACM T2 - KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 1255 KW - clicks KW - clustering AU - Makanju, Adetokunbo AU - Nur AU - Milios, Evangelos PY - 2009/// UR - http://dx.doi.org/10.1145/1557019.1557154 ER -