DocumentCode
498786
Title
An efficient algorithm based on time decay model for mining maximal frequent itemsets
Author
Huang, Guo-yan ; Wang, Li-bo ; Hu, Chang-zhen ; Ren, Jia-dong ; He, Hui-ling
Author_Institution
Coll. of Inf. Sci. & Eng., YanShan Univ., Qinhuangdao, China
Volume
4
fYear
2009
fDate
12-15 July 2009
Firstpage
2063
Lastpage
2066
Abstract
Mining maximal frequent itemsets is an active research area in data stream mining. A new algorithm, called MFI-TD (mine maximal frequent itemsets based on time decay model) is proposed for mining maximum frequent itemsets. A new data structure, called PW-tree (Point based Window-tree) is introduced to store each transaction for the current window, and the final node of the path which denotes a maximum frequent itemset is pointed by the DP (domain pointer). Then according to the data structure, the MFI-TD gradually reduces the weight of historical transaction supporting number, and deletes the obsolete and infrequent itemset branches in PW-tree by using of time decay model. Thus MFI-TD decreases the space complexity and reduces maintenance cost of PW-tree. Experimental results show that MFI-TD has better space efficiency and result accuracy than DSM-MFI algorithm.
Keywords
computational complexity; data mining; set theory; storage management; transaction processing; tree data structures; current window; data stream mining; data structure; domain pointer; mine maximal frequent itemset; point based window-tree; space complexity; time decay model; transaction storage; Cybernetics; Data mining; Data structures; Educational institutions; Fading; Information science; Itemsets; Machine learning; Machine learning algorithms; Space technology; Data stream; Maximal frequent itemsets; Time decay model;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212118
Filename
5212118
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