Sampling minimal frequent boolean (DNF) patterns

We tackle the challenging problem of mining the simplest Boolean patterns from categorical datasets. Instead of complete enumeration, which is typically infeasible for this class of patterns, we develop effective sampling methods to extract a representative subset of the minimal Boolean patterns (in disjunctive normal form – DNF). We make both theoretical and practical contributions, which allow us to prune the search space based on provable properties. Our approach can provide a near-uniform sample of the minimal DNF patterns. We also show that the mined minimal DNF patterns are very effective when used as features for classification. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications - Data Mining

• Type: Journal
• Year: 2012
• Where: KDD
• Authors: Geng Li, Mohammed J. Zaki
• Keywords: Data Mining, KDD 2012, Sampling Methods, Search Space, Subject Descriptors