A Study on the Application of Data Mining Techniques in the Management of Sustainable Education for Employment - Fang Fang (Data Science Journal, 2023)
With the gradual advancement of education management towards data and informationisation, how to establish a perfect employment education management system has become an important element of current student work. Data mining technology can extract valuable implicit information from the database, and is widely used in the practical processing of massive data. Based on the analysis of the characteristics of employment education management in universities, the study first improved the K-means algorithm by adding splitting and aggregation operations to it, used the improved K-means algorithm to cluster and analyse the employment education data, and then combined it with the optimised Apriori algorithm to further mine the useful information in the employment education data. The experimental outcomes demonstrate that the error rate of the improved K-means algorithm is stable at around 10%, with a high accuracy rate and strong stability; combined with the optimised Apriori algorithm applied to the employment education management system, the accuracy rate is basically maintained at over 96%, and the scores of students’ employment knowledge and employment practice are all over 90, indicating that the method can provide effective guidance for students’ employment and give a guideline for the sustainable education management of employment.