Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Mining Both Positive and Negative Impact-Oriented Sequential Rules from Transactional Data
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Non-redundant sequential rules-Theory and algorithm
Information Systems
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
The TIMERS II algorithm for the discovery of causality
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
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Mining sequential rules from sequence databases is an important research problem with wide applications. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. Moreover, a large proportion of sequential rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both by proposing an algorithm for mining top-k non redundant sequential rules.