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

页码：176 / 184

页数：9

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