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http://dx.doi.org/10.15207/JKCS.2020.11.9.001

Data Bias Optimization based Association Reasoning Model for Road Risk Detection  

Ryu, Seong-Eun (Data Mining Lab., Division of Computer Science and Engineering, Kyonggi University)
Kim, Hyun-Jin (Data Mining Lab., Division of Computer Science and Engineering, Kyonggi University)
Koo, Byung-Kook (Data Mining Lab., Division of Computer Science and Engineering, Kyonggi University)
Kwon, Hye-Jeong (Data Mining Lab., Division of Computer Science and Engineering, Kyonggi University)
Park, Roy C. (Department of Information Communication Software Engineering, Sangji University)
Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
Publication Information
Journal of the Korea Convergence Society / v.11, no.9, 2020 , pp. 1-6 More about this Journal
Abstract
In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.
Keywords
Association Reasoning; Context; Data Mining; Potential Road-risk; Data Optimization;
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