Clustering Analysis on the Introduction of Talents in Colleges
International Journal on Data Science and Technology
Volume 4, Issue 1, March 2018, Pages: 15-23
Received: Apr. 26, 2018; Published: Apr. 27, 2018
Views 197      Downloads 5
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
Article Tools
Follow on us
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.
Clustering Analysis, Dimensionality Reduction, Clustering Algorithm
To cite this article
Fang Dan, Chen Xinhui, Xi Xin, Clustering Analysis on the Introduction of Talents in Colleges, International Journal on Data Science and Technology. Vol. 4, No. 1, 2018, pp. 15-23. doi: 10.11648/j.ijdst.20180401.13
H. Li, Y. R. Liu, X. W. Jia, “Soft power study on higher education talent cultivation mode of international trade,” IBM, 2014, vol. 9, pp. 111-117.
D. Xu, “Study on the cultivating mode of undergraduate talents in tourism management: literature review, analysis and discussion,” JSSM, 2015, vol. 8, pp. 496.
Y. Ge, C. C. Che, J. F. Liu, et al., “Discussion and practice of application oriented personnel training system in university,” CSS, 2015, vol. 11, pp. 132-138.
M. P. Sendín-Hernández, C. Ávila-Zarza, C. Sanz, et al., “Cluster analysis identifies 3 phenotypes within allergic asthma,” The Journal of Allergy and Clinical Immunology: In Practice, 2017.
L. M. Brophy, J. E. Reece, and F. McDermott, “A cluster analysis of people on community treatment orders in Victoria, Australia,” Int J Law Psychiatry, 2006, vol. 29, pp. 469-481.
J. Eo, M. H. Kim, H. S. Bang, et al., “Effects of climate and landscape heterogeneity on the distribution of ground beetles (Coleoptera: Carabidae) in agricultural fields,” J. ASIA. PAC. ENTOMOL, 2016, vol. 19, pp. 1009-1014.
K. Adamczyk, D. Cywicka, P. Herbut, et al., “The application of cluster analysis methods in assessment of daily physical activity of dairy cows milked in the voluntary milking system,” COMPUT ELECTRON AGR, 2017, vol. 141, pp. 65-72.
N. Funtikova, A. A. Benítez-Arciniega, M. Fitó, et al., “Modest validity and fair reproducibility of dietary patterns derived by cluster analysis,” NUTR RES, 2015, vol. 35, pp. 265-268.
J. Halčinová, P. Trebuňa, I. Janeková, et al., “The proposal of stock items reconfiguration on the basis of cluster analysis results,” Procedia Eng, 2014, vol. 96, pp. 143-147.
Kijewska and A. Bluszcz, “Research of varying levels of greenhouse gas emissions in European countries using the k-means method,” ATMOS POLLUT RES, 2016, vol. 7, pp. 935-944.
L. H. Chen, Z. S. Xu, H. Wang, et al., “An ordered clustering algorithm based on K-means and the PROMETHEE method,” Int. J. Mach. Learn. & Cyber, 2016, 1-10.
Z. Fei and K. Liu, “Online process monitoring for complex systems with dynamic weighted principal component analysis,” Chin. J. Chem. Eng, 2016, vol. 24, pp. 775-786.
P. Rodríguez, R. Navarro, and J. J. Rozema, “Eigencorneas: application of principal component analysis to corneal topography,” OPHTHAL PHYSL OPT, 2014, vol. 34, pp. 667-677.
N. Goudarzi, D. Shahsavani, F. Emadi-Gandaghi, et al., “Quantitative structure-property relationships of retention indices of some sulfur organic compounds using random forest technique as a variable selection and modeling method,” J. Sep. Sci, 2016, vol. 39, pp. 3835-3842.
M. Meo, V. Zarzoso, O. Meste, et al., “Catheter ablation outcome prediction in persistent atrial fibrillation using weighted principal component analysis,” BIOMED SIGNAL PROCES, 2013, vol. 8, pp. 958-968.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931