Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidat...Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.展开更多
基金theFundoftheNationalManagementBureauofTraditionalChineseMedicine(No .2 0 0 0 J P 5 4 )
文摘Mining frequent pattern in transaction database, time series databases, and many other kinds of databases have been studied popularly in data mining research. Most of the previous studies adopt Apriori like candidate set generation and test approach. However, candidate set generation is very costly. Han J. proposed a novel algorithm FP growth that could generate frequent pattern without candidate set. Based on the analysis of the algorithm FP growth, this paper proposes a concept of equivalent FP tree and proposes an improved algorithm, denoted as FP growth * , which is much faster in speed, and easy to realize. FP growth * adopts a modified structure of FP tree and header table, and only generates a header table in each recursive operation and projects the tree to the original FP tree. The two algorithms get the same frequent pattern set in the same transaction database, but the performance study on computer shows that the speed of the improved algorithm, FP growth * , is at least two times as fast as that of FP growth.
