Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)

Analysis of Decision Tree Mining Algorithm Based on Improved Rough Set Classification

Authors
Lan Wang, Hongsheng Xu
Corresponding Author
Lan Wang
Available Online February 2017.
DOI
10.2991/emcm-16.2017.190How to use a DOI?
Keywords
Rough set; Decision tree; Data mining; Classification; ID3
Abstract

Classification is to find a set of models (functions) that describe the typical features of data set. In this paper, firstly, the rough set classification algorithm is improved. Then, analysis and application of classification algorithm based on improved rough set are described. This paper analyzes the advantages and disadvantages of the traditional decision tree algorithm, and puts forward the improvement method. The paper presents analysis of decision tree mining algorithm based on improved rough set classification.

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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Volume Title
Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
Series
Advances in Computer Science Research
Publication Date
February 2017
ISBN
10.2991/emcm-16.2017.190
ISSN
2352-538X
DOI
10.2991/emcm-16.2017.190How to use a DOI?
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/).

Cite this article

TY  - CONF
AU  - Lan Wang
AU  - Hongsheng Xu
PY  - 2017/02
DA  - 2017/02
TI  - Analysis of Decision Tree Mining Algorithm Based on Improved Rough Set Classification
BT  - Proceedings of the 2016 7th International Conference on Education, Management, Computer and Medicine (EMCM 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/emcm-16.2017.190
DO  - 10.2991/emcm-16.2017.190
ID  - Wang2017/02
ER  -