Abstract
Recently, an efficient method of database analysis using zero-suppressed binary decision diagrams (ZBDDs) has been proposed. BDDs are a graph-based representation of Boolean functions, now widely used in system design and verification. Here we focus on ZBDDs, a special type of BDDs, which are suitable for handling large-scale combinatorial itemsets in transaction databases. The ZBDD size greatly depends on the variable ordering used. In this paper, we propose a new method of ZBDD variable ordering for itemset mining of large-scale transaction databases, and show experimental results.
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