首页|基于群智能算法的物联网数据挖掘方法研究

基于群智能算法的物联网数据挖掘方法研究

扫码查看
为了提高物联网数据挖掘的准确性,提出了一种基于群智能算法的物联网数据挖掘方法,对粒子群算法进行改进,引入自适应的惯性权重因子,结合K-means聚类算法,进一步优化聚类中心的位置,使算法不易陷入局部最优,以提升算法的收敛性和准确性.经仿真实验证明,此算法能够有效提升物联网数据挖掘的准确性.
Research on Data Mining Method of Internet of Things Based on Swarm Intelligence Algorithm
In order to improve the accuracy of Internet of Things data mining,the study proposes an Internet of Things data mining method based on swarm intelligence algorithm,which improves the particle swarm optimization algorithm;introduces adaptive inertia weight factor;and combines K-means clustering algorithm to further optimize the location of clustering center,so that the algorithm is not easy to fall into local optimal.In this way,the convergence and accuracy of the algorithm is improved.Simulation results show that this algorithm can effectively improve the accuracy of data mining in the Internet of Things.

Data miningInternet of ThingsParticle swarm optimizationK-means clustering algorithm

陈洪波、邢磊

展开 >

山东华宇工学院,山东德州 253000

数据挖掘 物联网 粒子群算法 K-means聚类算法

2025

黑龙江科学
黑龙江省科学院

黑龙江科学

影响因子:1.014
ISSN:1674-8646
年,卷(期):2025.16(2)