Document Clustering Using Term Weights and Class Label Terms Based on Semantic Features
نویسندگان
چکیده
Clustering of class labels can be generated automatically, which is much lower quality than labels specified by human. In this paper, we propose a new enhancing document clustering method using terms of class label and term weights. The terms of class label can well represent the inherent structure of document clusters by non-negative matrix factorization (NMF). It can also 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.
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تاریخ انتشار 2012