LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets

By Takeaki Uno, Masashi Kiyomi, Hiroki Arimura. Workshop on Frequent Itemset Mining Implementations, 2004

479 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Sequential Pattern Mining (2017, background): “Although pattern mining has become very popular due to its applications in many domains, several pattern mining techniques such as those for frequent itemset mining [1, 53, 116, 86, 106] and association rule mining [1] are aimed at analyzing data, where the sequential ordering of events is not taken”
Optimal constraint-based decision tree induction from itemset lattices (2010, methodology): “We also include unmodiﬁed implementations of the frequent itemset miners Apriori (Agrawal et al. 1996), Eclat (Zaki et al. 1997a) and LCM (Uno et al. 2004) in the comparison.”