Title: Application of clustering algorithm and cloud computing in IoT data mining

Authors: Xu Wu

Addresses: Experimental Management Center, Chengyi College, Jimei University, Xiamen, 361021, China

Abstract: 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.

Keywords: density-based noise application spatial clustering algorithm; MapReduce cloud computing programming model; internet of things; IoT; data mining; internet of things monitoring system.

DOI: 10.1504/IJCC.2025.147443

International Journal of Cloud Computing, 2025 Vol.14 No.2, pp.183 - 199

Received: 26 Feb 2025
Accepted: 16 Apr 2025
Published online: 15 Jul 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article