Exceptional preferences mining

Cláudio Rebelo de Sá*, Wouter Duivesteijn, Carlos Soares, Arno Knobbe

*Corresponding author for this work

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

    8 Citations (Scopus)

    Abstract

    Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.

    Original languageEnglish
    Title of host publicationDiscovery Science
    Subtitle of host publication19th International Conference, DS 2016, Bari, Italy, October 19–21, 2016, Proceedings
    EditorsToon Calders, Michelangelo Ceci, Donato Malerba
    PublisherSpringer
    Pages3-18
    Number of pages16
    ISBN (Electronic)978-3-319-46307-0
    ISBN (Print)978-3-319-46306-3
    DOIs
    Publication statusPublished - 1 Jan 2016
    Event19th International Conference on Discovery Science, DS 2016 - Bari, Italy
    Duration: 19 Oct 201621 Oct 2016
    Conference number: 19

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer
    Volume9956
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349
    NameLecture Notes in Artificial Intelligence
    PublisherSpringer

    Conference

    Conference19th International Conference on Discovery Science, DS 2016
    Abbreviated titleDS 2016
    CountryItaly
    CityBari
    Period19/10/1621/10/16

    Keywords

    • Quality Measure
    • Ranking Behavior
    • Target Concept
    • Preference Matrix
    • Subgroup Discovery

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