Open AccessUniversity Ideological and Political Education Management Based on K-means Mean Value Algorithm
Author(s) -
MA Jia-feng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1852/4/042023
Subject(s) - informatization , ideology , computer science , process (computing) , service (business) , cluster analysis , political education , algorithm , data management , value (mathematics) , operations research , data mining , sociology , engineering management , knowledge management , process management , politics , mathematics , business , artificial intelligence , engineering , political science , marketing , machine learning , law , world wide web , operating system
With the continuous advancement of my country’s informatization construction process, many universities have established various business-based databases for daily management. As a widely used emerging discipline, the application prospects of analyze and extract data technology in university education informatization Well, it provides a brand-new and scientific analysis method for the absurdity of the management, construction, and service process of universities. Based on this, this article mainly studies the application of clustering technology in analyze and extract data in the management of ideological and political education (PE) in universities. This paper uses analyze and extract data technology to try and propose a university ideological PE management research based on the k-means cluster analysis method, using analyze and extract data on the basic functions of the traditional system to make secondary use of ideological and PE data. Optimize the iterative process of the algorithm of k-means, preprocess various data, use the algorithm of k-means in the division method, realize the cluster analysis of the data, and extract the valuable parts of the large amount of precipitated ideological and PE data. Establishing a data model and providing decision-making guidance to managers, scientifically managing the process of ideological and PE, can effectively improve the overall efficiency of ideological and PE.