Subjectively Interesting Subgroup Discovery on Real-valued Targets

Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie

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

5 Citations (Scopus)


Deriving insights from high-dimensional data is one of the core problems in data mining. The difficulty mainly stems from the large number of variable combinations to potentially consider. Hence, an obvious question is whether we can automate the search for interesting patterns. Here, we consider the setting where a user wants to learn as efficiently as possible about real-valued attributes. We introduce a method to find subgroups in the data that are maximally informative (in the Information Theoretic sense) with respect to one or more real-valued target attributes. The succinct subgroup descriptions are in terms of arbitrarily-Typed description attributes. The approach is based on the Subjective Interestingness framework FORSIED to use prior knowledge when mining most informative patterns.
Original languageEnglish
Title of host publicationProceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)
Number of pages4
ISBN (Electronic)9781538655207
Publication statusPublished - 2018
MoE publication typeA4 Conference publication
EventInternational Conference on Data Engineering - Paris, France
Duration: 16 Apr 201819 Apr 2018
Conference number: 34


ConferenceInternational Conference on Data Engineering
Abbreviated titleICDE


  • Exceptional Model Mining
  • Exploratory Data Mining
  • Pattern Mining
  • Subgroup Discovery
  • Subjective Interestingness


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