### Abstract

Original language | English |
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Title of host publication | Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018) |

Place of Publication | Piscataway |

Publisher | Institute of Electrical and Electronics Engineers |

Pages | 1352-1355 |

ISBN (Electronic) | 978-1-5386-5520-7 |

ISBN (Print) | 978-1-5386-5521-4 |

DOIs | |

Publication status | Published - 2018 |

Event | 2018 IEEE 34th International Conference on Data Engineering (ICDE) - Paris, France Duration: 16 Apr 2018 → 19 Apr 2018 |

### Conference

Conference | 2018 IEEE 34th International Conference on Data Engineering (ICDE) |
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Country | France |

City | Paris |

Period | 16/04/18 → 19/04/18 |

### Fingerprint

### Cite this

*Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)*(pp. 1352-1355). [8509369] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDE.2018.00148

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*Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018).*, 8509369, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1352-1355, 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 16/04/18. https://doi.org/10.1109/ICDE.2018.00148

**Subjectively interesting subgroup discovery on real-valued targets.** / Lijffijt, Jefrey; Kang, Bo; Duivesteijn, W.; Puolamäki, Kai; Oikarinen, Emilia; de Bie, Tijl.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Subjectively interesting subgroup discovery on real-valued targets

AU - Lijffijt, Jefrey

AU - Kang, Bo

AU - Duivesteijn, W.

AU - Puolamäki, Kai

AU - Oikarinen, Emilia

AU - de Bie, Tijl

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1109/ICDE.2018.00148

DO - 10.1109/ICDE.2018.00148

M3 - Conference contribution

SN - 978-1-5386-5521-4

SP - 1352

EP - 1355

BT - Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -