An improved algorithm of mining from FP-tree
Yong Qiu, Yong-Jie Lan, Qingsong Xie · 2005
Discovering association rules is a basic problem in data mining. Finding frequent itemsets is the most expensive step in association rule discovery. Analysing a frequent pattern growth (FP-growth) method is efficient and scalable for mining both long and short frequent patterns without candidate generation. And proposing a new efficient algorithm QFP-growth not only heirs all the advantages in FP-growth method, but also avoids its bottleneck in generating a huge number of conditional FP-trees. By using the technology of temporary root, QFP-growth reduces the processing time and memory space for mining frequent itemsets significantly. Performance study also shows that the QFP-growth method is efficient and scalable for mining large databases or data warehouses. Moreover, the algorithm generates frequent itemsets in order so that the result can be used expediently.