Maintaining knowledge about temporal intervals
Communications of the ACM
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
Time Series Abstraction Methods - A Survey
Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI)
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Local pattern detection and clustering
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
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In knowledge discovery from time series abstractions (like piecewise linear representations) are often preferred over raw data. In most cases it is implicitly assumed that there is a single valid abstraction and that the abstraction method, which is often heuristic in nature, finds this abstraction. We argue that this assumption does not hold in general and that there is need for knowledge discovery methods that pay attention to the ambiguity of features: In a different context, an increasing segment may be considered as (being part of) a decreasing segment. It is not a priori clear which view is correct or meaningful. We show that the relevance of ambiguous features depends on the relevance of the knowledge that can be discovered by using the features. We combine techniques from multiscale signal analysis and interval sequence mining to discover rules about dependencies in multivariate time series.