Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

  title={Infrequent Weighted Itemset Mining Using Frequent Pattern Growth},
  author={Luca Cagliero and Paolo Garza},
  journal={IEEE Transactions on Knowledge and Data Engineering},
Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost function, discovering rare data correlations is more interesting than mining frequent ones. This paper tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI) mining problem. Two novel quality measures are proposed to drive the IWI mining process… CONTINUE READING

2 Figures & Tables



Citations per Year

76 Citations

Semantic Scholar estimates that this publication has 76 citations based on the available data.

See our FAQ for additional information.