SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
Selecting the right objective measure for association analysis
Information Systems - Knowledge discovery and data mining (KDD 2002)
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
TSP: Mining top-k closed sequential patterns
Knowledge and Information Systems
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
Non-redundant sequential rules-Theory and algorithm
Information Systems
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Efficient incremental mining of top-K frequent closed itemsets
DS'07 Proceedings of the 10th international conference on Discovery science
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
Prediction mining – an approach to mining association rules for prediction
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
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
TNS: mining top-k non-redundant sequential rules
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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Mining sequential rules requires specifying parameters that are often difficult to set (the minimal confidence and minimal support). Depending on the choice of these parameters, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information. This is a serious problem because in practice users have limited resources for analyzing the results and thus are often only interested in discovering a certain amount of results, and fine-tuning the parameters can be very time-consuming. In this paper, we address this problem by proposing TopSeqRules, an efficient algorithm for mining the top-k sequential rules from sequence databases, where k is the number of sequential rules to be found and is set by the user. Experimental results on real-life datasets show that the algorithm has excellent performance and scalability.