Performance Prediction Model Based on K-Means Clustering Algorithm

Abstract

AbstractWith the development of the times, more and more fields will gradually realize the objectivity and convenience of using data to describe the characteristics of characters. The essence of the key user profile of accurate services is data mining and natural language processing, of which the more widely used Including short text classification processing method, keyword extraction method, fusion pruning algorithm, TF-IDF algorithm and other methods. How to solve the problem of different data standards, complete ability and job matching, maximize employee value, achieve objective evaluation of employee performance, comprehensively record employee work behavior and abilities, scientifically improve company A’s employee profile model, and use big data technology to manage human resources Empowerment is the core and key of A company's talent training and value drive. Through a large number of literature readings and experimental investigations, this paper gives a detailed overview of the performance model research under the flexible organizational structure of A company, the task-driven OKR of A company, and the establishment of the AI-based company A employee in-depth portrait platform. And use the K-Means clustering algorithm to establish a performance prediction model, and carry out experimental simulation and performance analysis. The K-means clustering algorithm is applied to the flow chart and experimental simulation and system performance test of the group connection point aggregation formation control. The experimental results show that the K-means clustering algorithm in the model process is applied to the flow chart of the cluster connection point aggregation formation control with accurate parameter data, which greatly improves the efficiency of human resource management. KeywordsK-Means clustering algorithmPerformance predictionModel researchPerformance algorithm