Discovery of Negative Association Rules from Uncertain Databases

Abstract

Uncertain data mining attracts much attention recently. In this paper, as an extension of probabilistic association rules, we propose probabilistic negative association rules to capture strong relationships between presence and absence of itemsets in uncertain databases. An algorithm is developed for extracting probabilistic negative association rules, which combines probabilistic frequent itemsets stored in a postfix tree.