Temporal Association Rules Mining in T-databases Using Pipeline Technique

Temporal data mining is rapidly evolving area of research that is at the intersection of several disciplines, including statistic, temporal pattern recognition, temporal database, optimization visualization, high performance computing & parallel computing. The presence of a temporal association rule may suggest a number of interpretations, such as; Past event (PE) → Future event (FE); The event(E) → PE and FE; events → coincidental (c) Classical association rules have no notion of order, while time implies an ordering. If we could find the associability of time with event, nothing will be hidden to us as the events are associated to each other in the form PE→P_tE (present event)→FE. In this study, we examine the association rules mining in temporal database. After partitioning the database, a time interval TI=[s,e] is allocated to each partition and sequentially put the partitions in an array, in reverse order.