Online Association Rule Mining

@inproceedings{Hidber1999OnlineAR,
  title={Online Association Rule Mining},
  author={Christian Hidber},
  booktitle={SIGMOD Conference},
  year={1999}
}
We present a novel algorithm to compute large itemsets online. The user is free to change the support threshold any time during the first scan of the transaction sequence. The algorithm maintains a superset of all large itemsets and for each itemset a shrinking, deterministic interval on its support. After at most 2 scans the algorithm terminates with the precise support for each large itemset. Typically our algorithm is by an order of magnitude more memory efficient than Apriori or DIC. 
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