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Classification and clustering are frequently-used methods in data excavation technology. This paper introduces the idea of text clustering into the categorization algorithm study. The authors also attempt to use the text categorization pattern of self'-initiated learning to design a clustering-based text categorization algorithm, in the purpose of reducing the dimension of training set and raising...
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...
Document Clustering is a widely studied problem in Text Categorization. It is the process of partitioning or grouping a given set of documents into disjoint clusters where documents in the same cluster are similar. K-means, one of the simplest unsupervised learning algorithms, solves the well known clustering problem following a simple and easy way to classify a given data set through a certain number...
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