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Document clustering is the process of partitioning a set of unlabeled n documents into clusters such that documents in each cluster share some common concepts. Each concept is conveniently represented by some key terms. Using words as features, text data are represented as a vector in a very high dimensional vector space. However, most documents are sparse vectors, for example, more than ten thousand...
Document clustering is the process of partitioning a set of unlabeled documents into clusters such that documents within each cluster share some common concepts. To help with this analysis, concepts are conveniently represented using some key terms. For clustering algorithm, the most costly CPU time has to do with the classification phase. Using words as features, text data are represented in a very...
Document clustering is the process to partition a set of unlabelled documents into some clusters. To analyze the documents efficiently and effectively, it is expected that all documents in each cluster have some shared concept. The shared concept is most conveniently represented using some key terms. Many methods have been studied for selecting important key terms. However, most of them belong to...
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