Mining All Non-derivable Frequent Itemsets

@article{Calders2002MiningAN,
  title={Mining All Non-derivable Frequent Itemsets},
  author={Toon Calders and Bart Goethals},
  journal={ArXiv},
  year={2002},
  volume={cs.DB/0206004}
}
Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibitively large. To overcome this problem, recently several proposals have been made to construct a concise representation of the frequent itemsets, instead of mining all frequent itemsets. The main goal of this paper is to identify redundancies in the… 

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