The PSP approach for mining sequential patterns

被引：0
|
作者：
Masseglia, F
Cathala, F
Poncelet, P
机构：
[1] CNRS, LIM ESA 6077, F-13288 Marseille 9, France
[2] CNRS, LIRUM, UMR 5506, F-34392 Montpellier, France
[3] IUT, Aix En Provence, France
[4] Cemagref, Aix En Provence, France
来源：
PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY | 1998年 / 1510卷
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：
In this paper, we present an approach, called PSP, for mining sequential patterns embedded in a database. Close to the problem of discovering association rules, mining sequential patterns requires handling time constraints. Originally introduced in [3], the issue is addressed by the GSP approach [10]. Our proposal resumes the general principles of GSP but it makes use of a different intermediary data structure which is proved to be more efficient than in GSP.
引用
收藏
页码：176 / 184
页数：9
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