Unsupervised Learning for Document Classification: Feasibility, Limitation, and the Bottom Line
نویسندگان
چکیده
While unsupervised learning methods are usually proposed to handle document clustering, in the literature, there exist practices that apply these methods to document classification as well. This paper analyzes the feasibility and the limitation of such practice, and studies its efficacy through a preliminary case study on the Reuters-21578 document collection.
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تاریخ انتشار 2004