Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Intelligent Data Analysis: An Introduction
Intelligent Data Analysis: An Introduction
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
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
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
Handling Feature Ambiguity in Knowledge Discovery from Time Series
DS '02 Proceedings of the 5th International Conference on Discovery Science
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Evolutionary Rule Mining in Time Series Databases
Machine Learning
Mining for weak periodic signals in time series databases
Intelligent Data Analysis
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
A review on time series data mining
Engineering Applications of Artificial Intelligence
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
Learning actions in complex software systems
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Distance measure for querying sequences of temporal intervals
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
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Observing a binary feature over a period of time yields a sequence of observation intervals. To ease the access to continuous features (like time series), they are often broken down into attributed intervals, such that the attribute describes the series' behaviour within the segment (e.g. increasing, high-value, highly convex, etc.). In both cases, we obtain a sequence of interval data, in which temporal patterns and rules can be identified. A temporal pattern is defined as a set of labeled intervals together with their interval relationships described in terms of Allen's interval logic. In this paper, we consider the evaluation of such rules in order to find the most informative rules. We discuss rule semantics and outline deficiencies of the previously used rule evaluation. We apply the J-measure to rules with a modified semantics in order to better cope with different lengths of the temporal patterns. We also consider the problem of specializing temporal rules by additional attributes of the state intervals.