关键词: 数据挖掘, 非负矩阵分解, 社区发现

Abstract: Nonnegative matrix factorization (NMF) has good ability in extracting inherent patterns and structures in high dimensional data and has been one of hot research topics in data mining. Nonnegative matrix factorization is a tool for unsupervised learning and has been widely applied in pattern recognition, text mining, image processing and bioinformatics. Recently, many researchers have paid attention to network-based data mining via nonnegative matrix factorization in order to detect cohesively connected community in networks. This paper summarizes community detection algorithms using nonnegative matrix factorization, including unsupervised methods and semi-supervised algorithms. Then, this paper compares and analyzes the performance of different algorithms by conducting experiments on artificial networks and real-world networks. Finally, this paper discusses challenges and further work on detecting communities in networks by using nonnegative matrix factorization.

Key words: data mining, nonnegative matrix factorization, community detection