Application of clustering algorithm and cloud computing in IoT data mining

In order to improve the accuracy of data mining in the internet of things system and shorten the time of data mining, this study first uses the mapping protocol cloud computing programming model to optimise the density based noise application spatial clustering algorithm. Then it improves the current data mining technology based on the optimised algorithm, and finally uses the improved data mining technology to mine the data in the internet of things, so as to improve the efficiency of data mining in the internet of things. The improved algorithm is applied to an IoT monitoring system, showing excellent performance in extracting data features and eliminating noise with a 100% removal rate. The system identifies abnormal data in just 0.9 ms with 100% accuracy. These results demonstrate that the enhanced data mining technique significantly improves mining efficiency, laying a foundation for better service quality and commercial value in IoT applications.