1 Introduction
Association rule mining (ARM) [16], [22] is essential in discovering correlations within large datasets. ARM is related to the Frequent Itemset Mining (FIM) problem which determines sets of items (i.e., itemsets) that appear frequently in a dataset. Formally, let \$\mathbf {I} = \lbrace i_1, i_2, \ldots, i_n\rbrace\$ be a set of \$n\$ items and let \$\mathcal {D} = [t_1, t_2, \ldots, t_t]\$ be a dataset of \$t\$ transactions, where each transaction comprises of a set of items, i.e., \$t_i \subseteq \mathbf {I}\$. The support of an itemset \$X \subseteq \mathbf {I}\$, denoted by \$\sigma (X)\$, is the number of transactions \$t_i\$ that include \$X\$ (i.e., \$X \subseteq t_i\$). Given threshold \$\sigma _0\$, FIM finds itemsets \$X\$ such that \$\sigma (X) \geq \sigma _0\$, called \$\sigma _0\$-frequent items.