Mining Quantitative Frequent Itemsets Using Adaptive Density-Based Subspace Clustering
Takashi Washio, Yuki Mitsunaga, Hiroshi Motoda · 2006
A novel approach to subspace clustering is proposed to exhaustively and efficiently mine quantitative frequent item-sets (QFIs) from massive transaction data. For the computational tractability, our approach introduces adaptive density-based and Apriori-like algorithm. Its outstanding performance is shown through numerical experiments.