NDLI: Mining Recent Frequent Itemsets in Data Streams

Content Provider	IEEE Xplore Digital Library
Author	Kun Li Yong-yan Wang Ellahi, M. Hong-an Wang
Copyright Year	2008
Description	Author affiliation: Intell. Eng. Lab., Inst. of Software Chinese Acad. of Sci., Beijing (Kun Li; Yong-yan Wang; Ellahi, M.; Hong-an Wang)
Abstract	Mining frequent itemsets in data streams is a hot research topic in recent years. Due to the continuous, high-speed and unbounded properties of data streams, traditional algorithms on static dataset are not suitable for mining in data streams. In this paper we present bounded frequent itemsets stream (abbreviated as BFI-stream) algorithm, which uses a prefix-tree based structure, called BFI-tree, to maintain all accurate frequent itemsets from sliding windows over data streams. By monitoring the boundary between frequent itemsets and infrequent itemsets, it restricts the update process on a small part of the tree. Mining all frequent itemsets with accurate frequencies is just to traverse the tree. It is time efficient even when the user-specified minimum support threshold is small. Experiments compare the time and space usage with MFI-TransSW, which also returns all accurate frequent itemsets from sliding windows. The results show that BFI-stream outperforms MFI-TransSW in both time and space at most time especially when the minimum support is small.
Starting Page	353
Ending Page	358
File Size	320884
Page Count	6
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.255
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering data mining Financial management Data engineering Telecommunication network management Data mining data stream Delay Itemsets frequent itemset Frequency Monitoring Fuzzy systems
Content Type	Text
Resource Type	Article

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