Unsupervised document clustering based on keyword clusters
Hsi-Cheng Chang, Chiun-Chich Hsu, Yi-Wen Deng · 2005
Due to the explosion growth of digital information, automatic document clustering or categorization has been an important research topic. Since document clustering has high dimension, the magnitude of the representation features will influence the efficiency and effect of the clustering and the precision of the clustering results. This paper presents an unsupervised document clustering method based on partitioning a weighted undirected graph. It initially discovers a set of tightly relevant keyword clusters that are disposed throughout the feature space of the collection of documents, and further clusters the documents into document clusters by using these keyword clusters. The experimental results show that the proposed approach can efficiently produce higher quality document clustering as compared with several well-known document clustering algorithms.