An optimisation of mobile terminal data mining method based on internet of things

In this paper, the optimisation of mobile terminal data mining method based on internet of things (IoT) is studied. Firstly, a framework for mobile terminal data mining optimisation is constructed, and mobile terminal data is collected by the mobile agent wireless sensor data acquisition technology. Then, the collected data are clustered by the chaotic search particle swarm K-means algorithm, and the clustered data are transmitted to the abnormal access detection module of mobile terminal users. The access detection module finally completes the mining of abnormal access behaviours of mobile terminal users by detecting the abnormal characteristics of user access behaviours, determining the abnormal type and checking the abnormal evolution. The experimental results show that the energy consumption of this method does not exceed 4J in a noisy environment, and this method is low in the data mining energy consumption and high in the accuracy. Copyright © 2024 Inderscience Enterprises Ltd.