Title: Effective information extraction of massive IoT big data based on adaptive spectral clustering algorithm

Authors: Lin Yu; Shumin Zhi

Addresses: Department of Health Management, Zhengzhou Health College, Zhengzhou, Henan, China ' Department of Health Management, Zhengzhou Health College, Zhengzhou, Henan, China

Abstract: To improve the standardised mutual information (NMI) and average accuracy (AA) of effective information extraction from IoT big data, a method for extracting effective information from massive IoT big data based on adaptive spectral clustering algorithm is proposed. Firstly, build a multi-layered IoT platform that utilises multiple sensors to collect and process multimodal data. Secondly, based on spatial discreteness, feature partitioning and differential evolution are performed on multi-type data models, and SVM algorithm is used for dimensionality reduction. Combining the aggregation tree frequent pattern set to analyse the correlation dimension, filtering node interference and extracting effective information features of networked big data. Finally, by using adaptive spectral clustering algorithm, combined with adaptive parameters and nearest neighbour path distance, effective information from networked big data can be accurately extracted. Experimental data shows that the NMI values of this method are all above 0.8, and the AA values are above 90%.

Keywords: spectral clustering; internet of things; massive data; effective information extraction.

DOI: 10.1504/IJIPT.2025.148570

International Journal of Internet Protocol Technology, 2025 Vol.18 No.3, pp.121 - 129

Received: 10 Mar 2025
Accepted: 02 Apr 2025
Published online: 12 Sep 2025 *

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