NDLI: LAPIN-SPAM: An Improved Algorithm for Mining Sequential Pattern

Content Provider	IEEE Xplore Digital Library
Author	Zhenglu Yang Kitsuregawa, M.
Copyright Year	2005
Description	Author affiliation: University of Tokyo (Zhenglu Yang)
Abstract	Sequence pattern mining is an important research problem because it is the basis of many other applications. Yet how to efficiently implement the mining is difficult due to the inherent characteristic of the problem - the large size of the data set. In this paper, by combining SPAM, we propose a new algorithm called LAst Position INduction Sequential PAttern Mining (abbreviated as LAPIN-SPAM), which can efficiently get all the frequent sequential patterns from a large database. The main difference between our strategy and the previous works is that when judging whether a sequence is a pattern or not, they use S-Matrix by scanning projected database (PrefixSpan) or count the number by joining (SPADE) or ANDing with the candidate item (SPAM). In contrast, LAPIN-SPAM can easily implement this process based on the following fact - if an items last position is smaller than the current prefix position, the item can not appear behind the current prefix in the same customer sequence. LAPIN-SPAM could largely reduce the search space during mining process and is considerable effectiveness in mining sequential pattern. Our experimental results show that LAPIN-SPAM outperforms SPAM up to three times on all kinds of dataset.
Starting Page	1222
Ending Page	1222
File Size	187397
Page Count	1
File Format	PDF
ISBN	0769526578
DOI	10.1109/ICDE.2005.235
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-04-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	TV Data analysis Databases Unsolicited electronic mail Cameras Data engineering Marketing and sales Partitioning algorithms Mining industry Pattern analysis
Content Type	Text
Resource Type	Article

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