Subjectively Interesting Subgroup Discovery on Real-valued Targets

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

  • Jefrey Lijffijt
  • Bo Kang
  • Wouter Duivesteijn
  • Kai Puolamäki

  • Emilia Oikarinen
  • Tijl De Bie

Organisaatiot

  • Ghent University
  • Eindhoven University of Technology

Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Data Engineering - Paris, Ranska
Kesto: 16 huhtikuuta 201819 huhtikuuta 2018
Konferenssinumero: 34

Conference

ConferenceInternational Conference on Data Engineering
LyhennettäICDE
MaaRanska
KaupunkiParis
Ajanjakso16/04/201819/04/2018

ID: 27505098