Clustering Analysis on the Introduction of Talents in Colleges

Fang Dan; Chen Xinhui; Xi Xin

doi:doi:10.11648/j.ijdst.20180401.13

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Clustering Analysis on the Introduction of Talents in Colleges

Fang Dan, Chen Xinhui, Xi Xin

Published in International Journal on Data Science and Technology (Volume 4, Issue 1)

Received: 26 April 2018 Published: 27 April 2018

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Abstract

With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.

Published in	International Journal on Data Science and Technology (Volume 4, Issue 1)
DOI	10.11648/j.ijdst.20180401.13
Page(s)	15-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Clustering Analysis, Dimensionality Reduction, Clustering Algorithm

References

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APA Style

Fang Dan, Chen Xinhui, Xi Xin. (2018). Clustering Analysis on the Introduction of Talents in Colleges. International Journal on Data Science and Technology, 4(1), 15-23. https://doi.org/10.11648/j.ijdst.20180401.13

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ACS Style

Fang Dan; Chen Xinhui; Xi Xin. Clustering Analysis on the Introduction of Talents in Colleges. Int. J. Data Sci. Technol. 2018, 4(1), 15-23. doi: 10.11648/j.ijdst.20180401.13

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AMA Style

Fang Dan, Chen Xinhui, Xi Xin. Clustering Analysis on the Introduction of Talents in Colleges. Int J Data Sci Technol. 2018;4(1):15-23. doi: 10.11648/j.ijdst.20180401.13

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@article{10.11648/j.ijdst.20180401.13,
  author = {Fang Dan and Chen Xinhui and Xi Xin},
  title = {Clustering Analysis on the Introduction of Talents in Colleges},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {1},
  pages = {15-23},
  doi = {10.11648/j.ijdst.20180401.13},
  url = {https://doi.org/10.11648/j.ijdst.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180401.13},
  abstract = {With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.},
 year = {2018}
}

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TY  - JOUR
T1  - Clustering Analysis on the Introduction of Talents in Colleges
AU  - Fang Dan
AU  - Chen Xinhui
AU  - Xi Xin
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180401.13
DO  - 10.11648/j.ijdst.20180401.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180401.13
AB  - With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.
VL  - 4
IS  - 1
ER  -

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Author Information

Fang Dan

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Chen Xinhui

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Xi Xin

School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Fang Dan, Chen Xinhui, Xi Xin. (2018). Clustering Analysis on the Introduction of Talents in Colleges. International Journal on Data Science and Technology, 4(1), 15-23. https://doi.org/10.11648/j.ijdst.20180401.13

Copy | Download

ACS Style

Fang Dan; Chen Xinhui; Xi Xin. Clustering Analysis on the Introduction of Talents in Colleges. Int. J. Data Sci. Technol. 2018, 4(1), 15-23. doi: 10.11648/j.ijdst.20180401.13

Copy | Download

AMA Style

Fang Dan, Chen Xinhui, Xi Xin. Clustering Analysis on the Introduction of Talents in Colleges. Int J Data Sci Technol. 2018;4(1):15-23. doi: 10.11648/j.ijdst.20180401.13

Copy | Download

@article{10.11648/j.ijdst.20180401.13,
  author = {Fang Dan and Chen Xinhui and Xi Xin},
  title = {Clustering Analysis on the Introduction of Talents in Colleges},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {1},
  pages = {15-23},
  doi = {10.11648/j.ijdst.20180401.13},
  url = {https://doi.org/10.11648/j.ijdst.20180401.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180401.13},
  abstract = {With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Clustering Analysis on the Introduction of Talents in Colleges
AU  - Fang Dan
AU  - Chen Xinhui
AU  - Xi Xin
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180401.13
DO  - 10.11648/j.ijdst.20180401.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 15
EP  - 23
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180401.13
AB  - With the development of economy and technology, introducing and training talents have become the key driving force in the world which can enhance the competitive strength of the whole countries. Therefore, the strategies of strengthening the universities and colleges with more talented people and making efforts to implement the construction of “Double top” are put forward in the same time. Methods of clustering analysis have been widely used in the actual researches. In this study, an effective clustering analysis model by comparing the clustering analysis under different dimensionality reduction methods is established. Firstly, preprocess the data about talent introduction which is collected from Zhejiang University of Finance and Economics, and use Principal Component Analysis (PCA), Weighted Principal Component Analysis (Weighted-PCA) and Random Forest (RF) to reduce the dimensions of the data. Next, use K-means clustering algorithm and K-medoids clustering algorithm to cluster the preprocessed data. The classification results indicate that the K-medoids algorithm with Weighted-PCA is superior to other clustering methods in this illustrative case. In addition, the experiment divides talents into high-end talents and mid-end talents. By looking into the analysis of the characteristics of the clustering results, some targeted advices on the talents introduction in colleges can be provided.
VL  - 4
IS  - 1
ER  -

Copy | Download