Application of the K Nearest Neighbor Algorithm Based on Scaling Weight in Intelligent Attendance System

This paper aims at the research and analysis of indoor positioning technology for realizing non-perceptual attendance problem in intelligent attendance system. Firstly, the classical KNN (K Nearest Neighbor) algorithm is elaborated, and an improved scaled weight-based KNN (SW-KNN) algorithm is proposed. The algorithm is simulated and analyzed through experiments. The experimental results show that SW-KNN algorithm improves the positioning accuracy and reduces the error compared with the classical K-nearest neighbor algorithm. The fingerprint positioning algorithm can realize the non-perceptual attendance of students, and the application effect is better.