A fast classification method of mass data in Internet of things based on fuzzy clustering maximum tree algorithm

Article type: Research Article

Authors: Duan, Zhixia | Tang, Shuai^{; *}

Affiliations: Jiyuan Vocational and Technical College, JiYuan 459000, China

Correspondence: [*] Corresponding author. E-mail: Duan20220511@163.com.

Abstract: In order to improve the classification accuracy and shorten the classification time of mass data, a fast classification method of mass data in the Internet of things based on fuzzy clustering maximum tree algorithm is proposed. Reduce the dimension to process the mass data of the Internet of things, establish the time series of the mass data of the Internet of things, and complete the preprocessing of the mass data of the Internet of things. Extract the feature vector of the Internet of things mass data, and use the fuzzy clustering maximum tree algorithm to perform fuzzy clustering analysis on the Internet of things mass data, so as to realize the classification of the Internet of things mass data. The results show that the recall rate of the proposed method is as high as 97.5%, the root mean square error is only 0.030, and the classification time is only 12.3 ms.

Keywords: Fuzzy clustering maximum tree algorithm, principal component analysis, Internet of things, mass data, rapid classification

DOI: 10.3233/WEB-220045

Journal: Web Intelligence, vol. 21, no. 2, pp. 139-147, 2023