<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper uses big data technology to focus on the clustering and correlation analysis of students’ daily behavior. The daily management workflow of students is constructed by analyzing the main algorithms of big data technology and the application scope. The association degree rules are combined to calculate students’ associated behaviors’ minimum support and confidence degree. The clustering algorithm was used to classify the daily management patterns of students into learning, closed, and active types. The student daily management clustering process was improved by using Newton interpolation to improve the inheritance and compatibility of node changes. In the correlation analysis, the support of the association label for regular type 1 was 0.441 in support and 0.852 in confidence, and the support of more regular type 2 was 0.425 in support and 0.846 in confidence.</p>
</abstract>

This paper uses big data technology to focus on the clustering and correlation analysis of students’ daily behavior. The daily management workflow of students is constructed by analyzing the main algorithms of big data technology and the application scope. The association degree rules are combined to calculate students’ associated behaviors’ minimum support and confidence degree. The clustering algorithm was used to classify the daily management patterns of students into learning, closed, and active types. The student daily management clustering process was improved by using Newton interpolation to improve the inheritance and compatibility of node changes. In the correlation analysis, the support of the association label for regular type 1 was 0.441 in support and 0.852 in confidence, and the support of more regular type 2 was 0.425 in support and 0.846 in confidence.

Exploring the innovation path of daily management mode of college students in the new era based on big data technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper uses big data technology to focus on the clustering and correlation analysis of students’ daily behavior. The daily management workflow of students...

Exploring the innovation path of daily management mode of college students in the new era based on big data technology

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: -

Eingereicht: 10. Dez. 2022

Akzeptiert: 10. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00865

Schlüsselwörter
Big data technology, Cluster partitioning, Association analysis, Newton interpolation, Daily student management

© 2023 Lin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Exploring the innovation path of daily management mode of college students in the new era based on big data technology

Online veröffentlicht: 30. Okt. 2023

Seitenbereich: -

Eingereicht: 10. Dez. 2022

Akzeptiert: 10. Mai 2023

DOI: https://doi.org/10.2478/amns.2023.2.00865

SchlüsselwörterBig data technology, Cluster partitioning, Association analysis, Newton interpolation, Daily student management

© 2023 Lin Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Big data technology, Cluster partitioning, Association analysis, Newton interpolation, Daily student management