NDLI: An efficient algorithm based on time decay model for mining maximal frequent itemsets

Content Provider	IEEE Xplore Digital Library
Author	Guo-Yan Huang Li-Bo Wang Chang-Zhen Hu Jia-Dong Ren Hui-Ling He
Copyright Year	2009
Description	Author affiliation: College of Information Science and Engineering, YanShan University, QinHuangDao, 066004, China (Guo-Yan Huang; Li-Bo Wang; Jia-Dong Ren; Hui-Ling He) \|\| School of Computer Science and Technology, Beijing Institute of Technology, 100081, China (Chang-Zhen Hu)
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.
Starting Page	2063
Ending Page	2066
File Size	244968
Page Count	4
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212118
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Itemsets Space technology Machine learning Cybernetics Data structures Fading Machine learning algorithms Educational institutions Information science Time decay model Data stream Maximal frequent itemsets
Content Type	Text
Resource Type	Article

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