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Text Mining: Classification, Clustering, and Applications by Ashok Srivastava, Mehran Sahami

Text Mining: Classification, Clustering, and Applications Ashok Srivastava, Mehran Sahami ebook
Page: 308
Publisher: Chapman & Hall
ISBN: 1420059408, 9781420059403
Format: pdf

Text Mining: Classification, Clustering, and Applications. €� Of all the books listed here, this one includes the most Perl programming examples, and it is not as scholarly as the balance of the list. Wiley series on methods and applications in data mining. Moreover, developers of text or literature mining applications are working at a furious pace, in part because mapping the human genome led to an explosion of text-based genetic information. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. This technique usually consists of finite steps, such as parsing a text into separate words, finding terms and reducing them to their basics ("truncation") followed by analytical procedures such as clustering and classification to derive patterns within the structured data, and finally evaluation and interpretation of the output. As a result, several large and complicated genomics and proteomics databases exist. Text mining is a process including automatic classification, clustering (similar but distinct from classification), indexing and searching, entity extraction (names, places, organization, dates, etc.), statistically Practical text mining with Perl. We consider there to be three relevant applications of our text-mining procedures in the near future:. (Genomics refers to the molecular pathways); and (c) text mining to find "non-trivial, implicit, previously unknown" patterns (p. Two basic TM tasks are classification and clustering of retrieved documents.