Mining Positive and Negative Fuzzy Sequential Patterns in Large Transaction Databases

被引：8
|
作者：
Ouyang, Weimin [1 ,2 ]
Huang, Qinhua [1 ]
Luo, Shuanghu [2 ]
机构：
[1] Shanghai Univ Polit Sci & Law, Moden Educ Technol Ctr, Shanghai 200438, Peoples R China
[2] Shanghai Univ, Sch Engn & Comp Sci, Shanghai 200072, Peoples R China
来源：
FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/FSKD.2008.245
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Sequential patterns mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has two limitations. First, it can not concern quantitative attributes; second, only positive sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the first limitation. In this paper, we Put forward a discovery algorithm for mining negative sequential patterns to resolve the second limitation, and a discovery algorithm for mining both positive and negative fuzzy sequential patterns by combining these two approaches.
引用
收藏
页码：18 / +
页数：2
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