Journal Published Online: 09 February 2022
Volume 51, Issue 1

Design of an Automatic Monitoring Model for Real-Time Data Flow in Network Based on Fuzzy Clustering Analysis for Health Analysis

CODEN: JTEVAB

Abstract

The current automatic monitoring model of real-time data flow in network has poor data clustering ability, which leads to a poor data flow clustering effect and a high memory occupation rate when the model is applied. To solve this problem, based on the data processing point of view, this paper designs an automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis. The threshold sampling method is used to complete the dynamic sampling of real-time data in network. The Principal Component Analysis (PCA) method sorts out the eigenvectors in the data flow and solves them. According to the collected data flow samples, fuzzy clustering analysis algorithm combined with feature vector is used to realize the automatic monitoring of real-time data flow in network. So far, the design of automatic monitoring model for real-time data flow in network based on fuzzy clustering analysis is completed. The experimental results show that the fuzzy clustering model is better than the current model.

Author Information

Lv, Yang
Basic Education, Jiangxi Vocational College of Finance and Economics, Jiujiang City, Jiangxi Province, China
Pages: 11
Price: $25.00
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Details
Stock #: JTE20210462
ISSN: 0090-3973
DOI: 10.1520/JTE20210462