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WIREs Data Mining Knowl Discov

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The lattice‐based approaches for mining association rules: a review

Advanced Review

Tuong Le, Bay Vo

Published Online: May 20 2016

DOI: 10.1002/widm.1181

Full article on Wiley Online Library: HTML PDF

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The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice‐based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static databases including lattice building and rule generation. In addition, in today's online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed. WIREs Data Mining Knowl Discov 2016, 6:140–151. doi: 10.1002/widm.1181

This article is categorized under:

Algorithmic Development > Association Rules

FIL‐2009 for DB^e with minSup = 50%.

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Minimal non‐redundant association rules generation on node A.

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Association rules with interestingness measures generation on node A.

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Association rules generation on node A.

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FCIL‐2013 for DB^e with minSup = 50%.

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FCIL‐2005 for DB^e with minSup = 50%.

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FIL‐2014 for DB^e with minSup = 50%.

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FIL‐2011 for DB^e with minSup = 50%.

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