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Clustering, an supervised learning process is a challenging problem. Clustering result quality improves the overall structure. In this article, we propose an incremental stream of hierarchical clustering and improve the efficiency, reduce time consumption and accuracy of text categorization algorithm by forming an exact sub clustering. In this paper we propose a new method called multilevel clustering...
Extracting useful information from user generated text on the web is an important ongoing research in natural language processing, machine learning, and data mining. Online tools like emails, news groups, blogs, and web forums provide an effective communication platform for millions of users around the globe and also provide an added advantage of anonymity. Millions of people post information on different...
Traditional text learning algorithms need labeled documents to supervise the learning process, but labeling documents of a specific class is often expensive and time consuming. We observe it is convenient to use some keywords(i.e. class-descriptions) to describe class sometimes. However, short class-description usually does not contain enough information to guide classification. Fortunately, large...
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