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
相关论文
共 50 条
  • [1] Mining Positive and Negative Fuzzy Multiple Level Sequential Patterns in Large Transaction Databases
    Ouyang, Weimin
    Huang, Qinhua
    [J]. PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL I, 2009, : 500 - 504
  • [2] Mining negative sequential patterns in transaction databases
    Ouyang, Wei-Min
    Huang, Qin-Hua
    [J]. PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2007, : 830 - +
  • [3] Mining direct and indirect fuzzy sequential patterns in large transaction databases
    Ouyang, Weimin
    Huang, Qinhua
    [J]. ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF CONTEMPORARY INTELLIGENT COMPUTING TECHNIQUES, 2007, 2 : 180 - +
  • [4] Mining Positive and Negative Weighted Fuzzy Association Rules in Large Transaction Databases
    Ouyang, Weimin
    [J]. 2009 SECOND INTERNATIONAL SYMPOSIUM ON KNOWLEDGE ACQUISITION AND MODELING: KAM 2009, VOL 2, 2009, : 269 - 272
  • [5] Mining Rare Sequential Patterns in Large Transaction Databases
    Ouyang, Weimin
    [J]. PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ELECTRONIC TECHNOLOGY, 2016, 48 : 159 - 162
  • [6] Mining Direct and Indirect Fuzzy Multiple Level Sequential Patterns in Large Transaction Databases
    Ouyang, Weimin
    Huang, Qinhua
    Luo, Shuanghu
    [J]. ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS, PROCEEDINGS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2008, 5227 : 906 - +
  • [7] Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases
    Han, Jianchao
    Beheshti, Mohsen
    [J]. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2006, 10 (03) : 287 - 294
  • [8] Mining negative fuzzy sequential patterns
    Lin, Nancy P.
    Chen, Hung-Jen
    Hao, Wei-Hua
    Chueh, Hao-En
    Chang, Chung-, I
    [J]. NEW ADVANCES IN SIMULATION, MODELLING AND OPTIMIZATION (SMO '07), 2007, : 52 - +
  • [9] Incremental mining of sequential patterns in large databases
    Masseglia, F
    Poncelet, P
    Teisseire, M
    [J]. DATA & KNOWLEDGE ENGINEERING, 2003, 46 (01) : 97 - 121
  • [10] Mining Weighted a Closed Sequential Patterns in Large Databases
    Ren, Jia-Dong
    Yang, Jing
    Li, Yan
    [J]. FIFTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 5, PROCEEDINGS, 2008, : 640 - 644
12345