A GSP-based efficient algorithm for mining frequent sequences

被引：0

作者：

Zhang, MH ^{[1
]}

Kao, B ^{[1
]}

Yip, CL ^{[1
]}

Cheung, D ^{[1
]}

机构：

[1] Univ Hong Kong, Dept Comp Sci & Informat Syst, Hong Kong, Hong Kong, Peoples R China

来源：

IC-AI'2001: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS I-III | 2001年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies the problem of mining frequent sequences in transactional databases. In [6], Agrawal and Srikant proposed the GSP algorithm for extracting frequently occurring sequences. GSP is an iterative algorithm. It scans the database a number of times depending on the length of the longest frequent sequences in the database. The I/O cost is thus substantial if the database contains very long frequent sequences. In this paper, we extend the candidate generating function used by GSP and propose a new two-stage algorithm ATS. Our algorithm first mines a sample of the database to obtain a rough estimate of the frequent sequences and then refines the solution. Experiment results show that MFS saves I/O cost significantly compared with GSP.

引用

页码：497 / 503

页数：7

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