PHÂN CỤM DỮ LIỆU DỰA TRÊN MẠNG NƠ RON HỌC SÂU

Abstract

Data clustering is a fundamental problem in the field of computer science and has many practical applications, especially in big data analysis and data mining. Traditional clustering algorithms such as K-means, MeanShift have been applied for many years but still have many limitations related to clustering accuracy. This paper investigates deep neural network models, specifically AutoEncoder networks, to address the clustering problem. Experimental results on standard datasets demonstrate that the system achieves significantly higher clustering accuracy compared to traditional methods.
Keywords: AutoEncoder, data clustering, deep neural network.