document: Clustering event logs using iterative partitioning The importance of event logs , as a source of information in systems and network management can not 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 % .
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