"非负矩阵分解在聚类中的高效应用研究：算法与发展"

The paper "Computing Research - Application Research of Non-negative Matrix Factorization in Clustering.pdf" provides a detailed exploration of the use of non-negative matrix factorization (NMF) in cluster analysis. Cluster analysis involves categorizing data into distinct groups based on similarities, and NMF is a method of matrix decomposition that has proven effective in handling large amounts of data. The paper discusses the long-term development of NMF in clustering applications and emphasizes the importance of efficient algorithms in enabling the widespread use of NMF. However, the paper also acknowledges that current NMF methods may not fully meet the demands of large-scale data processing, thus calling for the development of new, efficient NMF methods. The study highlights the significance of NMF in cluster analysis and suggests the need for further research and development in this area.