Clustering Mining Method of College Students’ Employment Data Based on Feature Selection

A Career Recommendation Method for College Students Based on Occupational Values

“…They surveyed college students' requirements for employment services provided by career instruction center, summarized the required functions of employment management system, divided the system according to user types, and designed the corresponding system function modules. To figure out the trends and status of college students' employment, Qi [21] proposed a college student employment data clustering and mining method based on feature selection, and used it to analyze the employment market of college students and gave the employment data structure; then on this basis, the employment data of college students were subjected to normalized compression, and the sparse scoring method was adopted for data feature selection based on the preprocessed data of college student employment; after that, the online clustering algorithm was adopted to conduct deep mining and clustering on the employment data of college students. To cope with the uneven distribution and poor accuracy of employment resources, Qi [22] applied social network mining to the design of the employment resource allocation algorithm for college students, the author constructed a long-term evolution system and performed interference suppression on it using inter-cell interference randomization technology, inter-cell interference cancellation technology, and inter-cell interference coordination technology, then experiment was carried out to prove that the proposed method can optimize the allocation of employment resources to a certain extent with shorter task scheduling time and higher resource allocation accuracy and efficiency.…”

Big Data Analysis and Forecast of Employment Position Requirements for College Students

“…Affected by economic situations and the increasing number of college graduates, young people are facing greater employment pressure as the employer companies are posing higher requirements for talents [9][10][11][12][13][14][15][16][17]. The leading cause of this phenomenon is that the talent cultivation goal of colleges and universities is disconnected with the recruitment requirements of companies, and the education institutions have failed to understand the job position and skill requirements contained in the recruitment information of companies [18][19][20][21][22][23][24]. Based on big data analysis, we can borrow the help of natural language processing and machine learning to analyze the semi-structured or unstructured information of online recruitment text posted by employer companies and dig the requirement features of these employment positions in a fast, efficient, and intelligent manner, and this is a meaningful work with practical value.…”

Analysis on the Regional Spatial Distribution of Employment of College Students and Evaluation of Employment Quality

“…The metropolitan circles and urban agglomerations in a region have now become important spatial carriers of the employment pattern of college students. Full understanding and excavation of the spatial distribution characteristics of the employment pattern of college students in the region is the precondition for improving the employment quality of college students in the region and achieving the rapid development of regional economy [6][7][8][9][10][11][12][13][14]. Currently, there are certain differences in the employment agglomeration level of college students in a region, and such obvious gaps and competition among cities, counties and districts are the main reasons for the imbalance of employment quality among these sub-regions [15][16][17][18][19][20][21][22][23].…”