Research on Applications of Data Mining in Electronic Commerce

DOI

10.2991/etmhs-15.2015.303How to use a DOI?

Keywords

Data Mining; Electronic Commerce; Logistic Regression; Service Evaluation

Abstract

With the fast development in the subject of computer science and technology, the combination of machine learning algorithms and electronic business is needed. There may be some unpredictable but frequent problems such as delay in shipment, shipping errors caused by E-commerce participants’ low efficiency. There are problems will have a negative impact on enterprises participants end up. The efficiency of e-commerce is an important way for a proper evaluation of improving management. In this paper, we propose the theory of knowledge mining based on Rough Set Theory to handle the vague and inaccurate information about the evaluation of supplier and mine the law knowledge that exists between input variables and adverse position. The RST output is then used as the feature and sent to the Logistic regression (LR) to the electronic commerce website product grade. The proposed method, called RST-LR, the discretization process by the attribute values; the minimum attribute set filtering; evaluation criteria; the establishment of calculation accuracy of ranking and assessment system. We simulate and experiment the algorithm and illustrate the accuracy.

Copyright

© 2015, 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/).

Download article (PDF)

TY  - CONF
AU  - Xiuping Yang
PY  - 2015/03
DA  - 2015/03
TI  - Research on Applications of Data Mining in Electronic Commerce
BT  - Proceedings of the 2015 International Conference on Education Technology, Management and Humanities Science
PB  - Atlantis Press
SP  - 1370
EP  - 1374
SN  - 2352-5398
UR  - https://doi.org/10.2991/etmhs-15.2015.303
DO  - 10.2991/etmhs-15.2015.303
ID  - Yang2015/03
ER  -