適応的密度基準に基づく部分空間クラスタリングを用いた定量的多頻度アイテム集合のマイニング

抄録

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.