Author_Institution :
Inf. Technol. & Eng. Coll., Tianjin Univ. of Technol. & Educ., Tianjin, China
Abstract :
The decisive factor of the comprehensive strength of a college is the integrated intrinsic qualifications of its personnel. Its human resources management needs a technology to discover the valuable knowledge, identify the human developing stratagem and provide the support for decision. Base on the human resources data of two colleges in Tianjin, after data pretreatment, such as data integration, data clean, data transformation and data reduction, this paper finished clustering analysis applying the SOM algorithm, realized information visualization. In the process the algorithm is carefully chosen, the fitting number of input and output layer node is identified. After training and verification, the result is approved by the experts of management. It can represent correspondingly the 4 kinds of personnel-service and other, administration and teaching assistant, teaching and research. It can differentiate the personnel both in teaching and research, which is the distinct character of human resource in colleges. The data mining result presents the correct character and existing problem in college human resources management. The result will be the guidance rule on the employment, training and upgrading of the personnel in the college, providing some support to the decision of human resources management strategy of college as well.
Keywords :
data mining; data reduction; educational institutions; human resource management; pattern clustering; personnel; self-organising feature maps; SOM algorithm; clustering analysis; college human resources data mining; college human resources management; data clean; data integration; data pretreatment; data reduction; data transformation; human developing stratagem; information visualization; integrated intrinsic qualifications; personnel; teaching assistant; Clustering algorithms; Data mining; Education; Educational institutions; Humans; Information analysis; Management training; Personnel; Qualifications; Resource management; Clustering Analysis; Data Mining; Data Pretreatment; Human Resources; SOM Algorithm;
