상세정보
학술지
Understanding Postal Delivery Areas in the Republic of Korea Using Multiple Unsupervised Learning Approaches
- 발행일
- 202204
- 출처
- ETRI Journal, v.44 no.2, pp.232-243
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 협약과제
- 21HR3300, 우편물류 인프라 기술개발, 정훈
- 초록
- 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 제안 키워드
- Complex analysis, Learning approach, Republic of Korea, Workload Balancing, cluster analyses, engineering method, residential environment, unsupervised learning
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