Application value of data mining technology in ultra dense heterogeneous wireless networks

In the era of the internet, a large amount of data is constantly generated, which has led to the emergence of network data mining technology. To improve access network security and user network experience, data mining technology is applied to ultra dense heterogeneous wireless networks. A switching algorithm based on user personalised preferences is proposed and a network security prediction module based on data mining is designed. Experimental data shows that when the number of networks is 10,000, the computational time cost based on the multi-attribute vertical switching algorithm is 3.45 ms. The switching algorithm based on user consumption preferences has a computational time cost of 0.97 ms, saving approximately 71.9% of the time. When the number of users exceeds 200, the throughput of the predictive network security switching algorithm based on data mining exceeds that of the analytic hierarchy process switching algorithm. The blocking rate is lower, which can better achieve balanced network selection and improve user network experience.