Title: Online learning data mining method based on improved fuzzy clustering

Authors: Pei Li

Addresses: Wuxi Institute of Technology, Wuxi, China

Abstract: Aiming at the problems of low data mining precision, large feature classification error and slow mining speed in online learning data mining methods, a fast online learning data mining method based on improved fuzzy clustering is designed. First, analyse the internal and external factors in the process of online learning data generation. Then classify online learning data through three dimensions, determine key data features by singular value decomposition through value calculation, construct data feature extraction model and complete online learning data feature extraction. Finally, put the online learning data in the fuzzy universe, calculate the membership of different online learning data, calculate the distance centre of online learning data according to the clustering algorithm, and then build an improved fuzzy clustering mining model to complete the rapid mining of online learning data. The results show that the proposed method has high precision and fast speed.

Keywords: improved fuzzy clustering; online learning data; fast excavation; value; degree of membership; objective function.

DOI: 10.1504/IJCAT.2024.141361

International Journal of Computer Applications in Technology, 2024 Vol.74 No.1/2, pp.52 - 62

Received: 12 Oct 2023
Accepted: 13 Feb 2024
Published online: 09 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article