Mining quantitative frequent itemsets using adaptive density-based subspace clustering

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.

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