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
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;
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
DOI :
10.1109/ICMLC.2009.5212118