Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Temporal Data Mining for Wireless Sensor Networks
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
A Pattern Mining Approach for Classifying Multivariate Temporal Data
BIBM '11 Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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We propose a two-phased approach to learn pattern graphs, a powerful pattern language for complex, multivariate temporal data, which is capable of reflecting more aspects of temporal patterns than earlier proposals. The first phase aims at increasing the understandability of the graph by finding common substructures, thereby helping the second phase to specialize the graph learned so far to discriminate against undesired situations. The usefulness is shown on data from the automobile industry and the libras data set by taking the accuracy and the knowledge gain of the learned graphs into account.