Analysis of Employment Information of University Graduates through Data Mining

— The employment information of university graduates contains a lot of useful information that can provide guidance for employment. This paper studied the decision tree method in data mining and improved C4.5 with Taylor’s median theorem in order to further improve its computational efficiency. The...

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Vydané v:Automatic control and computer sciences Ročník 58; číslo 1; s. 58 - 65
Hlavný autor: Hu, Lihui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Moscow Pleiades Publishing 01.02.2024
Springer Nature B.V
Predmet:
Algorithms
Classification
Computer Science
Control Structures and Microprogramming
Data analysis
Data mining
Decision trees
Employment
Labor law
STEM education
University graduates
University students
employment information
decision tree
data mining
graduate
ISSN:0146-4116, 1558-108X
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Popis
Shrnutí:— The employment information of university graduates contains a lot of useful information that can provide guidance for employment. This paper studied the decision tree method in data mining and improved C4.5 with Taylor’s median theorem in order to further improve its computational efficiency. The information of the 2021 graduates of China West Normal University was used as an example for analysis. It was found that practical ability, English level, and computer level had a great influence on graduate destination. In addition, compared with the traditional C4.5 algorithm, the improved C4.5 algorithm was much more efficient. The calculation time of the improved C4.5 algorithm was 6.27% shorter than the traditional C4.5 algorithm when analyzing 50 000 data. The improved C4.5 algorithm had an average accuracy of 89.81% when analyzing 200 data. The experimental results demonstrate the reliability of the improved C4.5 algorithm for employment information analysis and its applicability in practical employment management.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0146-4116
1558-108X
DOI:10.3103/S0146411624010073