Approaches For Validating Frequent Episodes Based On Periodicity In Time-series Data

Abstract

There is ongoing research on sequence mining of time-series data. We study Hybrid Apriori, an interval-based approach to episode discovery that deals with different periodicities in time-series data. Our study identifies the anomaly in the Hybrid Apriori by confirming the false positives in the frequent episodes discovered. The anomaly is due to the folding phase of the algorithm, which combines periods in order to compress data. We propose a main memory based solution to distinguish the false positives from the true frequent episodes. Our algorithm to validate the frequent episodes has several alternatives such as the naïve approach, the partitioned approach and the parallel approach in order to minimize the overhead of validation in the entire episode discovery process and is also generalized for different periodicities. We discuss the advantages and disadvantages of each approach and do extensive experiments to demonstrate the performance and scalability of each approach.