Exceptionally monotone models : the rank correlation model class for Exceptional Model Mining

L. Downar, W. Duivesteijn

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

Exceptional Model Mining strives to find coherent subgroups of the dataset where multiple target attributes interact in an unusual way. One instance of such an investigated form of interaction is Pearson’s correlation coefficient between two targets. EMM then finds subgroups with an exceptionally linear relation between the targets. In this paper, we enrich the EMM toolbox by developing the more general rank correlation model class. We find subgroups with an exceptionally monotone relation between the targets. Apart from catering for this richer set of relations, the rank correlation model class does not necessarily require the assumption of target normality, which is implicitly invoked in the Pearson’s correlation model class. Furthermore, it is less sensitive to outliers. We provide pseudocode for the employed algorithm and analyze its computational complexity, and experimentally illustrate what the rank correlation model class for EMM can find for you on six datasets from an eclectic variety of domains.
Original languageEnglish
Pages (from-to)369-394
Number of pages26
JournalKnowledge and Information Systems
Volume51
Issue number2
DOIs
Publication statusPublished - 1 May 2017
Externally publishedYes

Keywords

  • Data mining
  • Exceptional Model Mining
  • Monotonicity
  • Rank correlation
  • Subgroup Discovery

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