Constraint-based sequential pattern mining: the pattern-growth methods

被引：139

作者：

Pei, Jian ^{[1
]}

Han, Jiawei

Wang, Wei

机构：

[1] Simon Fraser Univ, Sch Comp Sci, Burnaby, BC V5A 1S6, Canada

[2] Univ Illinois, Urbana, IL 61801 USA

[3] Fudan Univ, Shanghai 200433, Peoples R China

来源：

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS | 2007年 / 28卷 / 02期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会; 中国国家自然科学基金;

关键词：

sequential pattern mining; frequent pattern mining; mining with constraints; pattern-growth methods;

D O I：

10.1007/s10844-006-0006-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.

引用

页码：133 / 160

页数：28

共 24 条

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