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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/3614

Title: Text Mining
Authors: Jisha A.K.
Keywords: : topic tracking,classifiers,clustering,information retrieval,data mining
Issue Date: 22-Jun-2011
Abstract: The volume of information circulating in a typical enterprise continues to increase. Knowledge hidden in the information however, is not fully utilized, as most of the information is described in textual form (as sentences). A large amount of text information can be analyzed objectively and efficiently with Text Mining.The field of text mining has received a lot of attention due to the ever increasing need for managing the information that resides in the vast amount of available text documents. Text documents are characterized by their unstructured nature. Ever increasing sources of such unstructured information include the World Wide Web, biological databases, news articles, emails etc. Text mining is defined as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. A key element is the linking together of the extracted information together to form new facts or new hypotheses to be explored further by more conventional means of experimentation. As the amount of unstructured data increases, text-mining tools will be increasingly valuable. A future trend is integration of data mining and text mining into a single system, a combination known as duo-mining
URI: http://hdl.handle.net/123456789/3614
Appears in Collections:MTech 2010-2012 Batch

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