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

DOI

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.

Copyright

© 2017, 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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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  - Proceedings of the 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  - 10.2991/febm-17.2017.35
ID  - Ren2017/10
ER  -