Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Interestingness of association rules using symmetrical tau and logistic regression

    Access Status
    Fulltext not available
    Authors
    Mohd Shaharanee, Izwan
    Hadzic, Fedja
    Dillon, Tharam S
    Date
    2009
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Mohd Shaharanee, Izwan and Hadzic, Fedja and Dillon, Tharam S. 2009. Interestingness of association rules using symmetrical tau and logistic regression, in Ann Nicholson and Xiaodong Li (ed), AI 2009: Advances in artificial intelligence. pp. 422-431. Heidelberg: Springer.
    Source Title
    AI 2009: Advances in artificial intelligence
    DOI
    10.1007/978-3-642-10439-8_43
    ISBN
    9783642104381
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    Remarks

    The original publication is available at : http://www.springerlink.com

    URI
    http://hdl.handle.net/20.500.11937/23232
    Collection
    • Curtin Research Publications
    Abstract

    While association rule mining is one of the most popular data mining techniques, it usually results in many rules, some of which are not considered as interesting or significant for the application at hand. In this paper, we conduct a systematic approach to ascertain the discovered rules and provide a rigorous statistical approach supporting this framework. The strategy proposed combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non significant rules. A real world dataset is used to demonstrate how the proposed unified framework can discard many of the redundant or non significant rules and still preserve high accuracy of the rule set as a whole.

    Related items

    Showing items related by title, author, creator and subject.

    • Quality and interestingness of association rules derived from data mining of relational and semi-structured data
      Mohd Shaharanee, Izwan Nizal (2012)
      Deriving useful and interesting rules from a data mining system are essential and important tasks. Problems such as the discovery of random and coincidental patterns or patterns with no significant values, and the generation ...
    • Evaluation and optimization of frequent, closed and maximal association rule based classification.
      Shaharanee, I.; Hadzic, Fedja (2014)
      Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand. The algorithms for closed and ...
    • Ascertaining data mining rules using statistical approaches
      Mohd Shaharanee, I.; Dillon, Tharam S; Hadzic, Fedja (2009)
      Knowledge acquisition techniques have been well researched in the data mining community. Such techniques, especially when used for unsupervised learning, often generate a large quantity of rules and patterns. While many ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.