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Journal Article
Understanding Postal Delivery Areas in the Republic of Korea Using Multiple Unsupervised Learning Approaches
- Authors
- Keejun Han, Yeongwoong Yu, Dong-gil Na, Hoon Jung, Younggyo Heo, Hyeoncheol Jeong, Sunguk Yun, Jungeun Kim
- Issue Date
- 2022-04
- Citation
- ETRI Journal, v.44, no.2, pp.232-243
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- 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.
- KSP Keywords
- Complex analysis, Learning approach, Republic of Korea, Workload Balancing, cluster analyses, engineering method, residential environment, unsupervised learning
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
