A mining algorithm using property items extracted from sampled examples

Abstract

This paper proposes a mining algorithm for relational frequent patterns based on a bottom-up property extraction from examples. The extracted properties, called property items, are used to construct patterns by a level-wise way like Apriori. The property items are assumed to have a special form, which is defined in terms of mode declaration of predicates. The algorithm produces frequent itemsets as patterns without duplication in the sense of logical equivalence. It is implemented as a system called MAPIX and is evaluated with four different dataseis with comparison to WARMR. MAPIX had large advantage in runtime. © Springer-Verlag Berlin Heidelberg 2007.