Title: Social wireless network user big data mining based on Python platform and hierarchical clustering computing
Authors: Kun Wang; Xiangbo Liang
Addresses: College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, 430033, China; College of Mathematics and Computer Science, Xinyang Vocational and Technical College, Xinyang, Henan, 464000, China ' College of Mathematics and Computer Science, Xinyang Vocational and Technical College, Xinyang, Henan, 464000, China
Abstract: Human behaviour, because of its complexity, makes it very important and interesting to explore the law of human behaviour. In recent years, the online social network represented by online personal community, online dating network and social network makes the amount of data of network users surge. The era of big data online social network gives us unprecedented opportunities to study human behaviour. The development of information science, the emergence of computer and the development of modern data storage technology provide us with a new objective material basis for the study of human behaviour. Data mining is an interdisciplinary subject, involving statistics, pattern recognition, information retrieval, machine learning and other disciplines. Data mining has been paid more and more attention by domestic and foreign academic circles, and has become a research hotspot. Therefore, this paper studies social wireless network user big data mining based on hierarchical clustering computing, the system is implemented via Python and compared with the latest models. The convincing results have proven the effectiveness.
Keywords: Python; hierarchical clustering; big data; social nature; wireless network; user data; data mining.
International Journal of Networking and Virtual Organisations, 2021 Vol.25 No.1, pp.62 - 82
Received: 01 Sep 2020
Accepted: 20 Jan 2021
Published online: 17 Sep 2021 *