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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (12): 92-100    DOI: 10.11925/infotech.2096-3467.2017.0955
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Self-Explainable Reduction Method for Mixed Feature Data Modeling
Siwei Jiang1,2,Zhenping Xie1,2(),Meijie Chen1,2,Ming Cai3
1School of Digital Media, Jiangnan University, Wuxi 214122, China
2Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi 214122, China
3Center of Informatization Development and Management, Jiangnan University, Wuxi 214122, China
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[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.

Key wordsMixed Feature Data      Self-Explainable Reduction      Data Modeling      Data Mining     
Received: 22 September 2017      Published: 29 December 2017

Cite this article:

Siwei Jiang,Zhenping Xie,Meijie Chen,Ming Cai. Self-Explainable Reduction Method for Mixed Feature Data Modeling. Data Analysis and Knowledge Discovery, 2017, 1(12): 92-100.

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