Mining Quantitative Frequent Itemsets Using Adaptive Density-based Subspace Clustering

Abstract

A novel approach to subspace clustering is proposed to exhaustively and efficiently mine quantitative frequent itemsets (QFIs) from massive transaction data for quantitative association rule mining. The numeric part of a QFI is an axis-parallel and hyper-rectangular cluster of transactions in an attribute subspace formed by numeric items. For the computational tractability, our approach introduces adaptive density-based and Apriori-like subspace clustering. Its outstanding performance is demonstrated through the comparison with the past subspace clustering approaches and the application to practical and massive data.