Survey Of Text Mining: Clustering, Classification, And Retrieval

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Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

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Springer New York Berlin Heidelberg Hong Kong London Milan Paris Tokyo Michael W. Berry Editor Survey of Text Mining Clustering, Classification, and Retrieval Scanned by Velocity With 57 Illustrations Springer Michael W. Berry Department of Computer Science University of Tennessee 203 Claxton Complex Knoxville, TN 37996-3450, USA [email protected] Cover illustration: Visualization of three major clusters in the L.A. Times news database when document vectors are projected into the 3-D subspace spanned by the three most relevant axes determined using COV rescale. This figure appears on p. 118 of the text. Library of Congress Cataloging-in-Publication Data Survey of text mining : clustering, classification, and retrieval / editor, Michael W. Berry. p
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