【摘要】 针对物联网大数据聚类精度较低的问题,提出基于人工智能技术的物联网大数据挖掘算法。通过建立维度控制机制,生成物联网数据模式树,初步获得数据挖掘范围,按照物联网数据特征检测符合要求的数据,并对聚类出的特征数据完成标准化处理,依靠人工智能中的神经网络技术,获取数据挖掘结果。实验结果表明,F-measure值最大可以提高15.01%、17.52%,而RI值最大可以提高20.32%、25.03%,算法的聚类精度明显有所提升。更多还原

【Abstract】 Aiming at the problem of low clustering accuracy of big data in the Internet of things,a big data mining algorithm based on artificial intelligence technology is proposed.By establishing the dimension control mechanism,the Internet of things data pattern tree is generated and the data mining scope is obtained preliminarily.According to the Internet of things data characteristics,the qualified data is detected,the standardized processing of the clustered characteristic data is completed,and the data mining results are obtained by neural network technology in artificial intelligence.The experimental results show that the maximum F-measure value can be increased by 15.01% and 17.52%,while the maximum RI value can be increased by 20.32% and 25.03%.The clustering accuracy of the algorithm is significantly improved.更多还原