Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
An integrated framework on mining logs files for computing system management
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Towards informatic analysis of syslogs
CLUSTER '04 Proceedings of the 2004 IEEE International Conference on Cluster Computing
Using Hidden Semi-Markov Models for Effective Online Failure Prediction
SRDS '07 Proceedings of the 26th IEEE International Symposium on Reliable Distributed Systems
Proceedings of the ACM first Ph.D. workshop in CIKM
LogView: Visualizing Event Log Clusters
PST '08 Proceedings of the 2008 Sixth Annual Conference on Privacy, Security and Trust
Detecting large-scale system problems by mining console logs
Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
Symptom-based problem determination using log data abstraction
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Chukwa: a system for reliable large-scale log collection
LISA'10 Proceedings of the 24th international conference on Large installation system administration
A graphical representation for identifier structure in logs
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
Experience mining Google's production console logs
SLAML'10 Proceedings of the 2010 workshop on Managing systems via log analysis and machine learning techniques
Proceedings of the 2011 ACM Symposium on Applied Computing
Baler: deterministic, lossless log message clustering tool
Computer Science - Research and Development
Event log mining tool for large scale HPC systems
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
LogSig: generating system events from raw textual logs
Proceedings of the 20th ACM international conference on Information and knowledge management
An application of improved gap-BIDE algorithm for discovering access patterns
Applied Computational Intelligence and Soft Computing - Special issue on Awareness Science and Engineering
CAPRI: a tool for mining complex line patterns in large log data
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Searching similar segments over textual event sequences
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Classification of Log Files with Limited Labeled Data
Proceedings of Principles, Systems and Applications on IP Telecommunications
Structured and Interoperable Logging for the Cloud Computing Era: The Pitfalls and Benefits
UCC '13 Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing
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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%.