Mining Quantitative Frequent Itemsets Using Adaptive Density-based Subspace Clustering Takashi Washio, Yuki Mitsunaga, and Hiroshi Motoda   View Paper (PDF)   Return to Session 2: Clustering Schemes I Abstract A novel approach to subspace clustering is proposed to exhaustively and efficiently mine quantitative frequent itemsets (QFIs) from massive transaction dataš. For the computational tractability, our approach introduces adaptive density-based and Apriori-like algorithm. Its outstanding performance is shown through numerical experiments. ©2006 Association for Computing Machinery