Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Mining a class of complex episodes in event sequences
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
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Data mining is a task of extracting useful patterns/episodes from large databases. Sequence data can be modeled using episodes. An episode is serial if the underlying temporal order is total. An episode rule of associating two episodes suggests a temporal implication of the antecedent episode to the consequent episode. We present two mining algorithms for finding frequent and confident serial-episode rules with their ideal occurrence/window widths, if exist, in event sequences based on the notion of minimal occurrences constrained by constant and mean maximum gap, respectively. A preliminary empirical study that illustrates the applicability of the episode-rule mining algorithms is performed with a set of earthquake data.