DocumentCode
2144920
Title
Mining Algorithm of Maximal Frequent Itemsets Based on Position Lattice
Author
Li, Yuan ; Li, Jun ; An, Ning ; Han, Chong
Author_Institution
Network Inf. Center of, Henan Univ. Kaifeng, Kaifeng, China
fYear
2010
fDate
14-16 Aug. 2010
Firstpage
712
Lastpage
715
Abstract
Maximal frequent itemsets mining is a fundamental and important problem in many data mining applications. In this paper, we present GMPV, a depth first search algorithm, which accurately displays itemset based on position vector, for mining maximal frequent itemsets. In GMPV algorithm, the transaction database is mapped to a Boolean matrix. The methods superset checking and pruning based on support are also used to increase the algorithm efficiency. Our experiment results show that GMPV algorithm is very validity.
Keywords
data mining; matrix algebra; tree searching; Boolean matrix; GMPV; data mining; depth first search algorithm; maximal frequent itemsets; mining algorithm; position lattice; transaction database; Algorithm design and analysis; Data mining; Finite element methods; Itemsets; Lattices;
fLanguage
English
Publisher
ieee
Conference_Titel
Granular Computing (GrC), 2010 IEEE International Conference on
Conference_Location
San Jose, CA
Print_ISBN
978-1-4244-7964-1
Type
conf
DOI
10.1109/GrC.2010.22
Filename
5576048
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