Data mining of serial episode association rules using gap constrained minimal occurrences
Data mining is a task of extracting useful patterns/episodes from large databases. Sequence data can be modelled 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 sound and complete mining algorithms for finding frequent and confident serial episode association rules with their ideal occurrence/window widths, if existing, in event sequences based on the notion of minimal occurrences constrained by constant and mean maximum gap, respectively. Two empirical studies are summarised: one focused on the absence of dependencies of local maximum confidence on the ideal occurrence/window widths in synthetic random datasets, and the other on the applicability of the episode rule mining algorithms with a set of earthquake data.
Int. J. Bus. Intell. Data Min.