Abstract
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.
Cite
CITATION STYLE
Hidber, C. (1999). Online Association Rule Mining. In Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 145–156). Association for Computing Machinery. https://doi.org/10.1145/304182.304195
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