Clustering Analysis on the Introduction of Talents in Colleges

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.