Discovering partial periodic sequential association rules with time lag in multiple sequences for prediction

A periodic pattern indicates something persistent and predictable, so it is important to identify and characterize the periodicity. This paper presents an approach for mining partial periodic association rules in temporal databases. This approach allows the discovery of periodic episodes such that the events in an episode are not limited to a fixed order. Moreover, this approach treats the antecedent and consequent of a rule separately and allows time lag between them. Thus, rules discovered are useful in many applications for prediction. The approach is implemented using two algorithms based on two data structures, event-based linked list and window-based linked list.