Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)

Data Pre-Processing for Real-World E-Commerce Delivery Address Clustering

Authors
Yuan Zhang
Corresponding Author
Yuan Zhang
Available Online December 2017.
DOI
https://doi.org/10.2991/anit-17.2018.28How to use a DOI?
Keywords
E-Commerce, Data Pre-processing, Clustering, Logical Hierarchical Scraping
Abstract

Rapid growth of economy and popularization of electronic commerce have facilitated the development of logistics industry. Aiming to increase e-commerce logistics efficiency, extracting meaningful information from the complex raw data is essential. In real-world business operation, the order data should be pre-processed for the convenience of customer analysis and delivery route planning. This paper focuses on a real-world e-commerce company case, and provides an approach for pre-processing of raw address data with messy text structures.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
Series
Advances in Intelligent Systems Research
Publication Date
December 2017
ISBN
978-94-6252-447-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/anit-17.2018.28How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yuan Zhang
PY  - 2017/12
DA  - 2017/12
TI  - Data Pre-Processing for Real-World E-Commerce Delivery Address Clustering
BT  - Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
SP  - 164
EP  - 168
SN  - 1951-6851
UR  - https://doi.org/10.2991/anit-17.2018.28
DO  - https://doi.org/10.2991/anit-17.2018.28
ID  - Zhang2017/12
ER  -