Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Exploratory Social Network Analysis with Pajek
Exploratory Social Network Analysis with Pajek
Topographical proximity for mining network alarm data
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Analysis of Alarm Sequences in a Chemical Plant
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Mining complex event patterns in computer networks
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
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Alarm management is a research area that is growing rapidly on industrial automation. One of the major difficulties in alarm rationalization, in which the volume of generated alarms is reduced to an appropriate number so that a human being can handle them, is to identify patterns that might indicate unnecessary alarms in the middle of files and databases containing tens of thousands of daily records. This work presents a new approach to analyze alarm occurrences, combining several techniques, such as: sequence mining, association rules extraction with MNR (Minimum Non Redundant Association Rules), cross-correlation analysis, and complex network modeling for visualization. The combination of different techniques creates a more comprehensive alternative to the detection process. The solution's performance, in terms of accuracy, shows improvements over the current approaches, resulting in a more reliable and predictable alternative for identification of meaningful patterns.