Discovering a taste for the unusual: exceptional models for preference mining

Cláudio Rebelo de Sá* (Corresponding Author), Wouter Duivesteijn, Paulo Azevedo, Alípio Mário Jorge, Carlos Soares, Arno Knobbe

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
57 Downloads (Pure)

Abstract

Exceptional preferences mining (EPM) is a crossover between two subfields of data mining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where some preference relations between labels significantly deviate from the norm. It is a variant of subgroup discovery, with rankings of labels as the target concept. We employ several quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: two measures look for exceptional overall ranking behavior, one measure indicates whether a particular label stands out from the rest, and a fourth measure highlights subgroups with unusual pairwise label ranking behavior. We explore a few datasets and compare with existing techniques. The results confirm that the new task EPM can deliver interesting knowledge.

Original languageEnglish
Pages (from-to)1775-1807
Number of pages33
JournalMachine Learning
Volume107
Issue number11
DOIs
Publication statusPublished - 1 Nov 2018
Externally publishedYes

Keywords

  • UT-Hybrid-D
  • Exceptional model mining
  • Label ranking
  • Preference learning
  • Subgroup discovery
  • Distribution rules

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