Intelligent system design based on cloud platform and data mining
In the field of education,particular attention is paid to data security issues in cloud platforms,as they are directly re-lated to the protection of the privacy of students and teachers and the secure storage of teaching content.In this paper,the multi-label kernel mapping data dimensionality reduction method and least squares multi-classification twin support vector machine model are ap-plied,and the invasive weed optimization algorithm is combined.The experimental results show that the mining error is about 2%,and the energy saving is between 400J-500J,which is higher than the other two methods.The maximum mining time was 2.2 seconds,much faster than the 7.8 seconds and 8.4 seconds of other methods.The results show that the proposed method has obvious advantages in dealing with advanced persistent threat data on cloud computing platforms.
cloud platformdata miningtwin support vector machineinvasive weed optimization algorithm