Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
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
Discovering Temporal Patterns in Multiple Granularities
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
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LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Mining semantic structures in movies
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
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We consider the problem of knowledge induction from sequential or temporal data. Patterns and rules in such data can be detected using methods adopted from association rule mining. The resulting set of rules is usually too large to be inspected manually. We show that (amongst other reasons) the inadequacy of the pattern space is often responsible for many of these patterns: If the true relationship in the data is fragmented by the pattern space, it cannot show up as a peak of high pattern density, but the data is divided among many different patterns, often difficult to distinguish from incidental patterns. To overcome this fragmentation, we identify core patterns that are shared among specialized patterns. The core patterns are then generalized by selecting a subset of specialized patterns and combining them disjunctively. The generalized patterns can be used to reduce the size of the set of patterns. We show some experiments for the case of labeled interval sequences, where patterns consist of a set of labeled intervals and their temporal relationships expressed via Allen's interval logic.