Abstract
Many efficient association rule mining algorithms have been proposed in the literature. In this paper, we propose an algorithm FRM (Mining Frequent Itemsets by Frequent-Related Mechanism). Most of the studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate generation is still costly when there exist a large number of long patterns. FRM scans database only four times and it does not adopt the Apriori-like approach in mining process. It uses the frequent-related mechanism to generate the itemsets which are the most possible to be frequent and it eliminates a great number of infrequent itemsets. So FRM is very suitable to mine the databases whose record length is very long.
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Huang, JP., Kuo, HC. (2009). A Filtering Approach for Mining Frequent Itemsets. In: Chien, BC., Hong, TP. (eds) Opportunities and Challenges for Next-Generation Applied Intelligence. Studies in Computational Intelligence, vol 214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92814-0_10
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DOI: https://doi.org/10.1007/978-3-540-92814-0_10
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