Mining frequent itemsets from secondary memory

  title={Mining frequent itemsets from secondary memory},
  author={G{\"o}sta Grahne and Jianfei Zhu},
  journal={Fourth IEEE International Conference on Data Mining (ICDM'04)},
Mining frequent itemsets is at the core of mining association rules, and is by now quite well understood algorithmically for main memory databases. In this paper, we investigate approaches to mining frequent itemsets when the database or the data structures used in the mining are too large to fit in main memory. Experimental results show that our techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining. 
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