Evaluation of public servant execution based on data mining technique and multiple factors joint modeling analysis

Abstract

With the rapid development of computer science and technology, data mining modelling techniques have emerged and rapidly developed as an alternative powerful meta-learning tool to accurately and fast analyze the massive volume of data generated by modern applications. The combination of data analysis technique and evaluation of public servant execution is urgently needed. Improve the execution of public servants at the grass-roots level is one of the important link to strengthen the construction of authority administrative efficiency of administrative goals is very important. Enhance the execution must first cultivate advanced concept, armed with advanced execution concept to the vast number of public servants at the grass-roots level. The assessment of public execution has a lot of traditional methods and models can be used but there is limitation. The limitation could be concluded as the following. Carelessness or poor sensitivity, At the grassroots level, the implementation of the main body of the general public servants at the grass-roots level and they can perform in place, one of the important factor is whether the leader on the work division of labor, organization, management and supervision effectively. In this paper, we conduct research on evaluation of public servant execution based on data mining technique and joint modeling analysis of multiple factors under big data environment. Firstly, we introduce some state-of-the-art clustering algorithm to serve as the basis of our model. Combined with deep neural network and optimization modelling, we propose our support vector machine based data clustering algorithm through multiple factor modelling. Subsequently, we discuss the principles on evaluation of public servant execution and process management. In the experimental part, we conduct experiment on both data clustering based data pre-processing step and the evaluation of elements' weight for process management. The result indicates the most important factor for management and the feasibility and effectiveness of our proposed clustering method. Future potential research areas are also discussed in the final Section.