Proceedings of the Second International Conference On Economic and Business Management (FEBM 2017)

A detection algorithm of customer outlier data based on data mining technology

Authors
Jia Ren
Corresponding Author
Jia Ren
Available Online October 2017.
DOI
https://doi.org/10.2991/febm-17.2017.35How to use a DOI?
Keywords
outlier membership data; dempster/shafer evidence theory; algorithm; data fusion
Abstract
For the outlier data detection problem of customer transactional retail data in a large-scale chain supermarket, customer transaction data are detected by data mining technology and database technology, the sample data of abnormal customer behavior have been chosen in the customer transaction database, the abnormal customer behavior will be found out for outlier samples data fusion by the Dempster/Shafer evidence theory. The experimental result shows that the algorithm is more accurate and efficient than other algorithms to detect abnormal customer transactional retail behavior by the Dempster/Shafer evidence theory.
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Proceedings
Second International Conference On Economic and Business Management (FEBM 2017)
Part of series
Advances in Economics, Business and Management Research
Publication Date
October 2017
ISBN
978-94-6252-423-1
ISSN
2352-5428
DOI
https://doi.org/10.2991/febm-17.2017.35How 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  - Jia Ren
PY  - 2017/10
DA  - 2017/10
TI  - A detection algorithm of customer outlier data based on data mining technology
BT  - Second International Conference On Economic and Business Management (FEBM 2017)
PB  - Atlantis Press
SP  - 272
EP  - 278
SN  - 2352-5428
UR  - https://doi.org/10.2991/febm-17.2017.35
DO  - https://doi.org/10.2991/febm-17.2017.35
ID  - Ren2017/10
ER  -