Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining
Proceedings of the 1st international workshop on open source data mining …, 2005•dl.acm.org
For a transaction database, a frequent itemset is an itemset included in at least a specified
number of transactions. To find all the frequent itemsets, the heaviest task is the computation
of frequency of each candidate itemset. In the previous studies, there are roughly three data
structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of
these has its own advantage and disadvantage with respect to the density of the input
database. In this paper, we propose an efficient way to combine these three data structures …
number of transactions. To find all the frequent itemsets, the heaviest task is the computation
of frequency of each candidate itemset. In the previous studies, there are roughly three data
structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of
these has its own advantage and disadvantage with respect to the density of the input
database. In this paper, we propose an efficient way to combine these three data structures …
For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has its own advantage and disadvantage with respect to the density of the input database. In this paper, we propose an efficient way to combine these three data structures so that in any case the combination gives the best performance.