Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)

Cluster analysis of container station based on container application data

Authors
Shilin Li, Lingxi Zhu, Jun Liu, Qingying Lai, Xu Wang
Corresponding Author
Shilin Li
Available Online March 2018.
DOI
https://doi.org/10.2991/mecae-18.2018.85How to use a DOI?
Keywords
Railway Transportation, Data Mining, Analysis of Stations, Clustering Algorithm
Abstract
With the increase of the number of railway container manufacturers, the average maintenance cost of each factory's container is uneven. In order to evaluate the quality of the container, it is necessary to do cluster analysis of railway container stations, thus classify the container by the different stations the containers go through. This paper is based on the data of container application for nearly 10 years, and constructs the index system of station cluster analysis. Through the cargo ticket and maintenance information of the container, the index data of the station is obtained. Then compares the different results of each clustering algorithm and selects the best clustering algorithm. Finally analyze the data characteristics and the reason of grouping results.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
Part of series
Advances in Engineering Research
Publication Date
March 2018
ISBN
978-94-6252-493-4
DOI
https://doi.org/10.2991/mecae-18.2018.85How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Shilin Li
AU  - Lingxi Zhu
AU  - Jun Liu
AU  - Qingying Lai
AU  - Xu Wang
PY  - 2018/03
DA  - 2018/03
TI  - Cluster analysis of container station based on container application data
BT  - 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/mecae-18.2018.85
DO  - https://doi.org/10.2991/mecae-18.2018.85
ID  - Li2018/03
ER  -