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Traditional keyword-based document clustering techniques have limitations due to simple treatment of words and hard separation of clusters. In this paper, we introduce named entities as objectives into fuzzy document clustering, which are the key elements defining document semantics and in many cases are of user
This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for
Traditional information retrieval (IR) systems evaluate user queries and retrieve/rank documents based on matching keywords in user queries with words in documents.These exact word-matching and ranking approaches ignore too many relevant documents that do not contain the exact keywords as specified in a user query
Identifying time periods with a burst of activity related to a topic has been an important problem in analyzing time stamped documents. In this paper, we discuss methods to compute a hot spot of a given topic from a time stamped document set. We consider basic topics that contain one or more keywords as well as
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