A sliding windows based dual support framework for discovering emerging trends from temporal data

摘要

In this paper we present the dual support Apriori for temporal data (DSAT) algorithm. This is a novel technique for discovering jumping and emerging patterns (JEPs) from time series data using a sliding window technique. Our approach is particularly effective when performing trend analysis in order to explore the itemset variations over time. Our proposed framework is different from the previous work on JEP in that we do not rely on itemsets borders with a constrained search space. DSAT exploits previously mined time stamped data by using a sliding window concept, thus requiring less memory, minimum computational cost and very low dataset accesses. DSAT discovers all JEPs, as in "na?ve" approaches, but utilises less memory and scales linearly with large datasets sets as demonstrated in the experimental section.