Discovering patterns in sequences of events
Artificial Intelligence
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Identifying and Using Patterns in Sequential Data
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient discovery of unusual patterns in time series
New Generation Computing
An intelligent model based on TS NARX for process prediction and daignosis rule extraction
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
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We present a method for inducing a set of rules from time series data, which is originated from a monitored process. The proposed method is called MAPS (Mining Aberrant Patterns in Sequences) and it may be used in decision support or in control to identify faulty system states. It consists of four parts: training, identification, event mining and prediction. In order to improve the flexibility of the event identification, we employ fuzzy sets and propose a method that extracts membership functions from statistical measures of the time series. The proposed approach integrates fuzzy logic and event mining in a seamless way. Some of the existing event mining algorithms have been modified to accommodate the need of discovering fuzzy event patterns.