Mining association rules for label ranking

Cláudio Rebelo De Sá, Carlos Soares, Alípio Mário Jorge, Paulo Azevedo, Joaquim Costa

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    25 Citations (Scopus)


    Recently, a number of learning algorithms have been adapted for label ranking, including instance-based and tree-based methods. In this paper, we propose an adaptation of association rules for label ranking. The adaptation, which is illustrated in this work with APRIORI Algorithm, essentially consists of using variations of the support and confidence measures based on ranking similarity functions that are suitable for label ranking. We also adapt the method to make a prediction from the possibly conflicting consequents of the rules that apply to an example. Despite having made our adaptation from a very simple variant of association rules for classification, the results clearly show that the method is making valid predictions. Additionally, they show that it competes well with state-of-the-art label ranking algorithms.

    Original languageEnglish
    Title of host publicationAdvances in Knowledge Discovery and Data Mining
    Subtitle of host publication15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings
    EditorsJoshua Zhexue Huang, Longbing Cao, Jaideep Srivastava
    Place of PublicationBerlin, Heidelberg
    Number of pages12
    ISBN (Electronic)978-3-642-20847-8
    ISBN (Print)978-3-642-20846-1
    Publication statusPublished - 8 Jun 2011
    Event15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011 - Shenzhen, China, Shenzhen, China
    Duration: 24 May 201127 May 2011
    Conference number: 15

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349
    NameLecture Notes in Artificial Intelligence


    Conference15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011
    Abbreviated titlePAKDD 2011
    Other24-27 May 2011


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