关键词: 文本聚类, 非负矩阵分解, 层次聚类

Abstract: The goal of text clustering is to group the documents with similar content into a same cluster, while separate the documents with different contents. Till now, a lot of clustering algorithms have been proposed according to different requirements, however, text clustering is still an open problem because of the potential characteristics of text data: large volume, high dimension, sparse etc. This paper proposes a clustering method based on hierarchical non-negative matrix factorization. The new method keeps the merits of the original non-negative matrix factoriza-tion, simultaneously clustering documents and identifying key features for each cluster. At the same time, it can mine the hierarchical structure between clusters, and such structure is very useful in many real applications, e.g., news browser. The experimental results on real data, 20Newsgroups and Reuters-RCV1, show that the proposed method performs better than the existing popular methods.

Key words: text clustering, non-negative matrix factorization, hierarchical clustering