Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Loose Coupling of Failure Explanarion and Repair: Using Learning Goals to Sequence Learning Models
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Rapid on-line temporal sequence prediction by an adaptive agent
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Modeling Reuse on Case-Based Reasoning with Application to Breast Cancer Diagnosis
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
QSSI: A NEW SIMILARITY INDEX FOR QUALITATIVE TIME SERIES. APPLICATION TO CLASSIFY VOLTAGE SAGS
Applied Artificial Intelligence
eXiT*CBR: A framework for case-based medical diagnosis development and experimentation
Artificial Intelligence in Medicine
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In this paper we present a methodology based on combining sequence learning and case-based reasoning. This methodology has been applied in the analysis, mining and recognition of sequential data provided by complex systems with the aim of anticipating failures. Our objective is to extract valuable sequences from log data and integrate them on a case-based reasoning system in order to make predictions based on past experiences. We have used an Apriori---style algorithm (CloSpan) to extract patterns from original data. Afterwards, we have extended our own tool (eXiT*CBR) to deal with sequences in a case-based reasoning environment. The results have shown that our methodology anticipated correctly the failures in most of the cases.