Title: Effective information classification mining method for IoT big data based on support vector machine

Authors: Shumin Zhi

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

Abstract: The effective information classification mining of IoT big data is of great significance in enhancing data value, optimising resource allocation and promoting technological innovation. Based on the research motivation of solving the problems of low recall and precision, and long completion time in current methods, an effective information classification mining method for IoT big data based on support vector machine is proposed. By optimising the selection of initial k-values and initial clustering centre points, the improved K-means clustering algorithm is utilised to achieve big data collection in the Internet of Things. By predicting residuals for anomaly detection and removal of IoT data, the removed IoT big data is input into a fuzzy support vector machine to achieve information classification mining. Experimental results show that the average recall rate of the proposed method is 97.07%, the average precision rate is 96.72%, and the task completion time varies between 0.22 s and 0.68 s.

Keywords: support vector machine; IoT; big data; effective information; classification mining; improved K-means clustering algorithm.

DOI: 10.1504/IJIPT.2025.150236

International Journal of Internet Protocol Technology, 2025 Vol.18 No.4, pp.175 - 185

Received: 24 Feb 2025
Accepted: 29 Apr 2025
Published online: 04 Dec 2025 *

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