The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining

Junlong Zhang; Dan Zhao; Huijie Wang

doi:doi:10.11648/j.ijdst.20180401.12

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The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining

Junlong Zhang, Dan Zhao, Huijie Wang

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

To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.

Published in	International Journal on Data Science and Technology (Volume 4, Issue 1)
DOI	10.11648/j.ijdst.20180401.12
Page(s)	6-14
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

Data Mining, Outlier Excavation, Machine Learning, Talent Identification

References

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[3]	M. M. Breuing, H. P. Kriegel and R. T. Ng, “LOF: identifying density-based local outliers,” ACM Sigmord Record, 2000, vol. 29, pp. 93-104.
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[5]	L. V. Utkin, A. I. Chekh and Y. A. Zhuk, “Binary classification svm-based algorithms with interval-valued training data using triangular and epanechnikov kernels,” Neural Networks, 2016, vol. 80, pp. 53-66.
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APA Style

Junlong Zhang, Dan Zhao, Huijie Wang. (2018). The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. International Journal on Data Science and Technology, 4(1), 6-14. https://doi.org/10.11648/j.ijdst.20180401.12

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ACS Style

Junlong Zhang; Dan Zhao; Huijie Wang. The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. Int. J. Data Sci. Technol. 2018, 4(1), 6-14. doi: 10.11648/j.ijdst.20180401.12

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AMA Style

Junlong Zhang, Dan Zhao, Huijie Wang. The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. Int J Data Sci Technol. 2018;4(1):6-14. doi: 10.11648/j.ijdst.20180401.12

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@article{10.11648/j.ijdst.20180401.12,
  author = {Junlong Zhang and Dan Zhao and Huijie Wang},
  title = {The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.ijdst.20180401.12},
  url = {https://doi.org/10.11648/j.ijdst.20180401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180401.12},
  abstract = {To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.},
 year = {2018}
}

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TY  - JOUR
T1  - The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining
AU  - Junlong Zhang
AU  - Dan Zhao
AU  - Huijie Wang
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180401.12
DO  - 10.11648/j.ijdst.20180401.12
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  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180401.12
AB  - To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.
VL  - 4
IS  - 1
ER  -

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Author Information

Junlong Zhang

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

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

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

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Plain Text BibTeX RIS

APA Style

Junlong Zhang, Dan Zhao, Huijie Wang. (2018). The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. International Journal on Data Science and Technology, 4(1), 6-14. https://doi.org/10.11648/j.ijdst.20180401.12

Copy | Download

ACS Style

Junlong Zhang; Dan Zhao; Huijie Wang. The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. Int. J. Data Sci. Technol. 2018, 4(1), 6-14. doi: 10.11648/j.ijdst.20180401.12

Copy | Download

AMA Style

Junlong Zhang, Dan Zhao, Huijie Wang. The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining. Int J Data Sci Technol. 2018;4(1):6-14. doi: 10.11648/j.ijdst.20180401.12

Copy | Download

@article{10.11648/j.ijdst.20180401.12,
  author = {Junlong Zhang and Dan Zhao and Huijie Wang},
  title = {The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining},
  journal = {International Journal on Data Science and Technology},
  volume = {4},
  number = {1},
  pages = {6-14},
  doi = {10.11648/j.ijdst.20180401.12},
  url = {https://doi.org/10.11648/j.ijdst.20180401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180401.12},
  abstract = {To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - The Outliers and Prediction Analysis of University Talents Introduced Based on Data Mining
AU  - Junlong Zhang
AU  - Dan Zhao
AU  - Huijie Wang
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijdst.20180401.12
DO  - 10.11648/j.ijdst.20180401.12
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  - 6
EP  - 14
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20180401.12
AB  - To create profits for colleges and universities, introduction of talents is an important indicator of the value evaluation of talent introduction in colleges and universities. It can meet the needs of the large data system demand for abnormal detection and prediction in the process of talent introduction. In this article, after reducing the dimension of data by principal component analysis, using the method based on distance (markov distance), the method based on density (local outlier factor) and the method based on clustering (two-step, k-means), we establish the outlier detection model. We find 15 significant outliers and find that the publication of SSCI papers and the experience in C9 institutions have a significant effect on obtaining National Foundation of China. Finally, we use support vector machine, decision tree (C4.5, C5.0), bayes, and random forest to establish the talent prediction model after eliminating abnormal values. By comparing four methods, we find that support vector machine method and decision tree method’s prediction accuracies are higher. After optimization, their accuracies can reach 75.00% and 72.09% respectively.
VL  - 4
IS  - 1
ER  -

Copy | Download