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Erschienen in:
2007 | OriginalPaper | Buchkapitel
LAPIN: Effective Sequential Pattern Mining Algorithms by Last Position Induction for Dense Databases
verfasst von : Zhenglu Yang, Yitong Wang, Masaru Kitsuregawa
Erschienen in: Advances in Databases: Concepts, Systems and Applications
Verlag: Springer Berlin Heidelberg
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Sequential pattern mining is very important because it is the basis of many applications. Although there has been a great deal of effort on sequential pattern mining in recent years, its performance is still far from satisfactory because of two main challenges: large search spaces and the ineffectiveness in handling dense datasets. To offer a solution to the above challenges, we have proposed a series of novel algorithms, called the LAst Position INduction (LAPIN) sequential pattern mining, which is based on the simple idea that the last position of an item,
α
, is the key to judging whether or not a frequent k-length sequential pattern can be extended to be a frequent (k+1)-length pattern by appending the item
α
to it. LAPIN can largely reduce the search space during the mining process, and is very effective in mining dense datasets. Our performance study demonstrates that LAPIN outperforms PrefixSpan [4] by up to an order of magnitude on long pattern dense datasets.