A Rough Set Approach to Building Association Rules and Its Applications

Junzo Watada, Takayuki Kawaura, Hao Li

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)

    Abstract

    Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.

    Original languageEnglish
    Title of host publicationIntelligent Systems Reference Library
    Pages203-218
    Number of pages16
    Volume13
    DOIs
    Publication statusPublished - 2011

    Publication series

    NameIntelligent Systems Reference Library
    Volume13
    ISSN (Print)18684394
    ISSN (Electronic)18684408

    Fingerprint

    Association rules
    Data mining
    Values
    Marketing
    data analysis
    marketing
    confidence
    Rough set
    simulation
    experiment
    Experiments
    evidence
    Data base

    Keywords

    • association rule
    • data mining
    • Rough set

    ASJC Scopus subject areas

    • Computer Science(all)
    • Information Systems and Management
    • Library and Information Sciences

    Cite this

    Watada, J., Kawaura, T., & Li, H. (2011). A Rough Set Approach to Building Association Rules and Its Applications. In Intelligent Systems Reference Library (Vol. 13, pp. 203-218). (Intelligent Systems Reference Library; Vol. 13). https://doi.org/10.1007/978-3-642-19820-5_10

    A Rough Set Approach to Building Association Rules and Its Applications. / Watada, Junzo; Kawaura, Takayuki; Li, Hao.

    Intelligent Systems Reference Library. Vol. 13 2011. p. 203-218 (Intelligent Systems Reference Library; Vol. 13).

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Watada, J, Kawaura, T & Li, H 2011, A Rough Set Approach to Building Association Rules and Its Applications. in Intelligent Systems Reference Library. vol. 13, Intelligent Systems Reference Library, vol. 13, pp. 203-218. https://doi.org/10.1007/978-3-642-19820-5_10
    Watada J, Kawaura T, Li H. A Rough Set Approach to Building Association Rules and Its Applications. In Intelligent Systems Reference Library. Vol. 13. 2011. p. 203-218. (Intelligent Systems Reference Library). https://doi.org/10.1007/978-3-642-19820-5_10
    Watada, Junzo ; Kawaura, Takayuki ; Li, Hao. / A Rough Set Approach to Building Association Rules and Its Applications. Intelligent Systems Reference Library. Vol. 13 2011. pp. 203-218 (Intelligent Systems Reference Library).
    @inbook{e4e152645fb04af5910e6830f658fe8a,
    title = "A Rough Set Approach to Building Association Rules and Its Applications",
    abstract = "Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.",
    keywords = "association rule, data mining, Rough set",
    author = "Junzo Watada and Takayuki Kawaura and Hao Li",
    year = "2011",
    doi = "10.1007/978-3-642-19820-5_10",
    language = "English",
    isbn = "9783642198199",
    volume = "13",
    series = "Intelligent Systems Reference Library",
    pages = "203--218",
    booktitle = "Intelligent Systems Reference Library",

    }

    TY - CHAP

    T1 - A Rough Set Approach to Building Association Rules and Its Applications

    AU - Watada, Junzo

    AU - Kawaura, Takayuki

    AU - Li, Hao

    PY - 2011

    Y1 - 2011

    N2 - Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.

    AB - Data mining is a process or method of finding information, evidence, insight, knowledge and hypotheses in a huge database, such as marketing data. Recently, the association rule presented by R. Agrawal in 1983 has been used to rapidly expand a data mining method. This method is general and flexible and can be applied to both general data analysis and very wide surveys. In addition, the rules for this method are complicated. On the other hand, when the support value is minimal and the confidence value is high, the obtained value is already known and trivial. A breakthrough method is needed. The objective of this paper is to present a rough set model to overcome such issues. Employing the rough set model, we analyzed three different scales of databases and compared the results of simulation experiments using proposed and conventional models. The rough set model obtained an efficient number of association rules and usually took less computation time.

    KW - association rule

    KW - data mining

    KW - Rough set

    UR - http://www.scopus.com/inward/record.url?scp=84885447838&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84885447838&partnerID=8YFLogxK

    U2 - 10.1007/978-3-642-19820-5_10

    DO - 10.1007/978-3-642-19820-5_10

    M3 - Chapter

    AN - SCOPUS:84885447838

    SN - 9783642198199

    VL - 13

    T3 - Intelligent Systems Reference Library

    SP - 203

    EP - 218

    BT - Intelligent Systems Reference Library

    ER -