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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Understanding postal delivery areas in the Republic of Korea using multiple unsupervised learning approaches

  • Han, Keejun (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Yu, Yeongwoong (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Na, Dong-gil (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Jung, Hoon (Intelligent Convergence Research Laboratory, Electronics and Telecommunications Research Institute) ;
  • Heo, Younggyo (Department of Computer Science and Engineering, Kongju National University) ;
  • Jeong, Hyeoncheol (Department of Computer Science and Engineering, Kongju National University) ;
  • Yun, Sunguk (Department of Computer Science and Engineering, Kongju National University) ;
  • Kim, Jungeun (Department of Computer Science and Engineering, Kongju National University)
  • Received : 2021.10.28
  • Accepted : 2022.02.20
  • Published : 2022.04.10
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Abstract

Changes in household composition and the residential environment have had a considerable impact on the features of postal delivery regions in recent years, resulting in a large increase in the overall workload of domestic postal delivery services. In this paper, we provide complex analysis results for postal delivery areas using various unsupervised learning approaches. First, we extract highly influential features using several feature-engineering methods. Then, using quantitative and qualitative cluster analyses, we find the distinctive traits and semantics of postal delivery zones. Unsupervised learning approaches are useful for successfully grouping postal service zones, according to our findings. Furthermore, by comparing a postal delivery region to other areas in the same group, workload balancing was achieved.

Keywords

Acknowledgement

Institute for Information and Communications Technology Promotion, Grant/Award Number: 1711125741; Institute of Information & communications Technology Planning & Evaluation (IITP); Korea government (MSIT), Grant/Award Number: 2018-0-01664

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