earticle

영어

Clustering of class labels can be generated automatically, which is much lower quality than labels specified by human. If the class labels for clustering are provided, the clustering is more effective. In classic document clustering based on vector model, documents appear terms frequency without considering the semantic information of each document. The property of vector model may be incorrectly classified documents into different clusters when documents of same cluster lack the shared terms. To overcome this problem are applied by the knowledge based approaches. However, these approaches have an influence of inherent structure of documents on clustering and a cost problem of constructing ontology. In addition, the methods are limited to cluster suitable text document clustering from in exploding big text data on Cloud environment. In this paper, we propose a big text document clustering method using terms of class label and semantic feature based Hadoop. Class label term can well represent the inherent structure of document clusters by non-negative matrix factorization (NMF) based Hadoop. The proposed method can improve the quality of document clustering which uses the class label terms and the term weights based on term mutual information (TMI) with WordNet at a little cost. It also can cluster the big data size of document using the distributed parallel processing based on Hadoop. The experimental results demonstrate that the proposed method achieves better performance than other document clustering methods.