Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things

Article type: Research Article

Authors: Wang, Huixian^a | Zheng, Hongjiang^{b; *}

Affiliations: [a] School of Information Management, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China | [b] Shanghai Engineering Technology Research Center for Intelligent and Connected Vehicle Terminals, Shanghai 200030, China

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

Keywords: Improved ant colony algorithm, fuzzy theory, Internet of things, high dimensional abnormal data, deep data mining

DOI: 10.3233/WEB-210471

Journal: Web Intelligence, vol. 19, no. 4, pp. 253-262, 2021