<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Although research into big data technologies is advancing quickly, it has not yet been used for the “party building + political thinking” paradigm of grassroots mass organizations. The structure, purpose, and method of clustering analysis in data mining technology, as well as the various data types involved in clustering analysis technology, are studied in this paper after first analyzing the development status of data mining technology and the issues in the process of data mining. Secondly, we investigate the advantages of big data mining for “Party formation + Civic Affairs” at the local level, including four points, the management of grassroots party members, the education of party members, the ideological dynamics of party members and cadres, and the development of the relationship between party groups and teachers and students. Moreover, the “task assessment quantification table” from the “party building + thinking and politics” model was utilized as the data source, and cluster analysis was carried out using the k-mean technique. The percentages of the three categories of grades in the cluster assessment, which were 30%, 62%, and 8%, were found to be compatible with the percentages of the three categories of scores, which were 21%, 68%, and 11%. This study brings some reference significance to both party building and thinking and the government work of college counselors; this contributes to raising the bar for political thinking, party organization, and governance.</p>
</abstract>

Although research into big data technologies is advancing quickly, it has not yet been used for the “party building + political thinking” paradigm of grassroots mass organizations. The structure, purpose, and method of clustering analysis in data mining technology, as well as the various data types involved in clustering analysis technology, are studied in this paper after first analyzing the development status of data mining technology and the issues in the process of data mining. Secondly, we investigate the advantages of big data mining for “Party formation + Civic Affairs” at the local level, including four points, the management of grassroots party members, the education of party members, the ideological dynamics of party members and cadres, and the development of the relationship between party groups and teachers and students. Moreover, the “task assessment quantification table” from the “party building + thinking and politics” model was utilized as the data source, and cluster analysis was carried out using the k-mean technique. The percentages of the three categories of grades in the cluster assessment, which were 30%, 62%, and 8%, were found to be compatible with the percentages of the three categories of scores, which were 21%, 68%, and 11%. This study brings some reference significance to both party building and thinking and the government work of college counselors; this contributes to raising the bar for political thinking, party organization, and governance.

Building a “party building + political thinking” model for grassroots mass organizations in China in the era of big data

Applied Mathematics and Nonlinear Sciences

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

Although research into big data technologies is advancing quickly, it has not yet been used for the “party building + political thinking” paradigm of...

Building a “party building + political thinking” model for grassroots mass organizations in China in the era of big data

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 20. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00403

Schlüsselwörter
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement.

© 2023 Xingyuan Huang, published by Sciendo

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

Building a “party building + political thinking” model for grassroots mass organizations in China in the era of big data

Online veröffentlicht: 30. Sept. 2023

Seitenbereich: -

Eingereicht: 20. Dez. 2022

Akzeptiert: 10. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00403

SchlüsselwörterSpeech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement.

© 2023 Xingyuan Huang, published by Sciendo

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

Schlüsselwörter
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement.