Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials

Data Preprocessing Based on Partially Supervised Learning

Authors
Na Liu, Guanglai Gao, Guiping Liu
Corresponding Author
Na Liu
Available Online November 2016.
DOI
https://doi.org/10.2991/icimm-16.2016.121How to use a DOI?
Keywords
data preprocessing; Web Log Mining; rule; partially supervised learning
Abstract

Data preprocessing is the foundation to improve the quality of data mining and determines the effect of Web mining. Currently, data for mining is typically collected from the server, but data set from the client is more accurate. In order to better deal with these data, we propose a data preprocessing method based on partially supervised learning. The paper discusses in detail data cleaning process based on partially supervised learning, and conducts experiments to verify the validity of the method employed, and ultimately determines the optimal number of training documents.

Copyright
© 2016, 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 6th International Conference on Information Engineering for Mechanics and Materials
Series
Advances in Engineering Research
Publication Date
November 2016
ISBN
978-94-6252-244-2
ISSN
2352-5401
DOI
https://doi.org/10.2991/icimm-16.2016.121How to use a DOI?
Copyright
© 2016, 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  - Na Liu
AU  - Guanglai Gao
AU  - Guiping Liu
PY  - 2016/11
DA  - 2016/11
TI  - Data Preprocessing Based on Partially Supervised Learning
BT  - Proceedings of the 6th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 678
EP  - 683
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-16.2016.121
DO  - https://doi.org/10.2991/icimm-16.2016.121
ID  - Liu2016/11
ER  -