%A Jiang Siwei,Xie Zhenping,Chen Meijie,Cai Ming %T Self-Explainable Reduction Method for Mixed Feature Data Modeling %0 Journal Article %D 2017 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2017.0955 %P 92-100 %V 1 %N 12 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4457.shtml} %8 2017-12-25 %X

[Objective] This paper aims to mine the data with continuous numeric and label features. [Methods] We proposed a self-explainable reduction model to represent the data. The proposed model used the new reduction objective to create adaptive discrete division for continuous data dimension. [Results] We examined the new model with standard datasets and found it had better performance than the existing ones. [Limitations] The computational efficiency of the proposed method was not very impressive, which cannot meet the demand of large-scale data mining. [Conclusions] The proposed model is innovative and practical to model the mixed feature data.