kDCI: on using direct count up to the third iteration
Lucchese C., Orlando S., Perego R.
Frequent itemsets mining
We thus introduced such technique in the last version of kDCI, which is level-wise hybrid algorithm. kDCI stores the dataset with an horizontal format to disk during the first iterations. After some iteration the dataset may become small enough (thanks to anti-monotone frequency pruning) to be stored in the main memory in a vertical format, and after that the algorithm goes on performing tid-lists intersections to retrieve itemsets supports, and searches among candidates are not needed anymore. Usually the dataset happens to be small enough at most at the fourth iteration.
Source: ICDM Workshop on Frequent Itemset Mining Implementations, pp. 1–1, Brighton, UK, 1 November 2004
Publisher: CEUR-WS.org, Aachen, DEU
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@inproceedings{oai:it.cnr:prodotti:91777,
title = {kDCI: on using direct count up to the third iteration},
author = {Lucchese C. and Orlando S. and Perego R.},
publisher = {CEUR-WS.org, Aachen, DEU},
booktitle = {ICDM Workshop on Frequent Itemset Mining Implementations, pp. 1–1, Brighton, UK, 1 November 2004},
year = {2004}
}
0000-0002-2545-0425