@inproceedings{9bc372c536eb4d5391c77dc00eabc07a,
title = "An efficient algorithm based on time decay model for mining maximal frequent itemsets",
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 = "Data stream, Maximal frequent itemsets, Time decay model",
author = "Huang, \{Guo Yan\} and Wang, \{Li Bo\} and Hu, \{Chang Zhen\} and Ren, \{Jia Dong\} and He, \{Hui Ling\}",
year = "2009",
doi = "10.1109/ICMLC.2009.5212118",
language = "English",
isbn = "9781424437030",
series = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "2063--2066",
booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",
address = "United States",
note = "8th International Conference on Machine Learning and Cybernetics, ICMLC 2009 ; Conference date: 12-07-2009 Through 15-07-2009",
}