Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)

Classification of Land Production Function Based on Cluster Model

Authors
Xiao He, Kui Fang, Xinghui Zhu
Corresponding Author
Xiao He
Available Online November 2017.
DOI
10.2991/amms-17.2017.3How to use a DOI?
Keywords
functions classification; cluster analysis; land
Abstract

Scientific classification of land production functions can promote the efficient use of land. In this paper,16 land production functions were collected, and 10 characteristic features of land production function were put forward according to the different attributes of each function. The characteristics of the land production functions are assigned to obtain the land production function score table,Then use the hierarchical clustering and K-means clustering analysis those data, and the results of three kinds of hierarchical clustering and one K-means clustering were obtained.By comparing with the results of sequence classification, 16 land production functions are classified into three categories.

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 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
Series
Advances in Intelligent Systems Research
Publication Date
November 2017
ISBN
978-94-6252-433-0
ISSN
1951-6851
DOI
10.2991/amms-17.2017.3How 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  - Xiao He
AU  - Kui Fang
AU  - Xinghui Zhu
PY  - 2017/11
DA  - 2017/11
TI  - Classification of Land Production Function Based on Cluster Model
BT  - Proceedings of the 2017 International Conference on Applied Mathematics, Modeling and Simulation (AMMS 2017)
PB  - Atlantis Press
SP  - 10
EP  - 13
SN  - 1951-6851
UR  - https://doi.org/10.2991/amms-17.2017.3
DO  - 10.2991/amms-17.2017.3
ID  - He2017/11
ER  -