Improved methods for extracting frequent itemsets from interim‐support trees

Abstract

Abstract:In this paper we present the Dual Support Apriori for Temporal data (DSAT) algorithm. This is a novel technique for discovering Jumping 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 "naïve" approaches, but utilises less memory and scales linearly with large datasets sets as demonstrated in the experimental section.

A fast parallel algorithm for frequent itemsets mining

“…The sequential algorithm that we use for parallelization uses a second tree structure, namely the T-tree on the nodes of which candidate frequent itemsets are stored. This structure was introduced in [6] and algorithms that use it are presented in [10]. Here we propose a modification of this structure as follows: each node of the tree in the initial structure had two pointers.…”