Flexible propositionalization of continuous attributes in relational data mining

作者：

Highlights：

• Our approach can handle thresholds on attributes and on the number of objects.

• Tackling numeric attributes with both absolute and relative numbers efficiently.

• Selecting the optimal combination of propositionalizer and classifier effectively.

• The proposed approach is flexible to be applied over different contexts.

• Experiments show the effectiveness and efficiency of the proposed approach.

摘要

•Our approach can handle thresholds on attributes and on the number of objects.•Tackling numeric attributes with both absolute and relative numbers efficiently.•Selecting the optimal combination of propositionalizer and classifier effectively.•The proposed approach is flexible to be applied over different contexts.•Experiments show the effectiveness and efficiency of the proposed approach.

论文关键词：Relational data mining,Propositionalization,Numeric attributes,Aggregation,Knowledge discovery

论文评审过程：Available online 29 May 2015, Version of Record 25 June 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.05.053