High-dimensional power enterprise employee clustering based on convolutional neural network and mini-batch K-means algorithm

With the gradual promotion of the use of mobile portal APPS in various enterprises and the rapid increase of users, the demand for personalized recommendation applications for different categories of users is gradually put forward. In actual application scenarios where users have no clear labels in advance, effective clustering algorithm is of great significance to realize personalized recommendation for users. Most of the traditional clustering algorithms are not suitable for enterprise application scenarios with millions of users, and the algorithm mainly focuses on the calculation process, with little consideration for the improvement of features. This paper focuses on the situation of employees of State Grid Corporation of China using "iGuoWang" mobile portal APP, proposes a clustering method based on hybrid deep learning for employees of electric power enterprises, counts the number of employees using each application, screens effective clustering features from the perspective of statistical analysis, and obtains effective clustering data sets. A "convolutional neural network + Mini-Batch K-means" clustering model was designed, and experiments were carried out based on the model and other traditional clustering algorithms. The results show that the clustering effect of the proposed model is significantly improved compared with other traditional algorithms under the evaluation indexes of contour coefficient and Calinski-Harabasz value. In view of the clustering results, this paper briefly analyzes the statistical indicators of various categories and proves that the clustering model has certain reference significance for personalized application recommendation.