Subjectively Interesting Subgroup Discovery on Real-valued Targets
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A4 Artikkeli konferenssijulkaisussa
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Date
2018
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en
Pages
4
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Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018), pp. 1356-1359
Abstract
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.Description
| openaire: EC/H2020/665501/EU//PEGASUS-2
Keywords
Exceptional Model Mining, Exploratory Data Mining, Pattern Mining, Subgroup Discovery, Subjective Interestingness
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Citation
Lijffijt, J, Kang, B, Duivesteijn, W, Puolamäki, K, Oikarinen, E & De Bie, T 2018, Subjectively Interesting Subgroup Discovery on Real-valued Targets. in Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018)., 8509369, IEEE, pp. 1356-1359, International Conference on Data Engineering, Paris, France, 16/04/2018. https://doi.org/10.1109/ICDE.2018.00148