Survey of Text Mining - Clustering, Classification, and Retrieval (Hardcover, 2004 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	September 2003
First published:	2004
Editors:	Michael W. Berry
Dimensions:	235 x 155 x 15mm (L x W x T)
Format:	Hardcover
Pages:	244
Edition:	2004 ed.
ISBN-13:	978-0-387-95563-6
Categories:	Books > Reference & Interdisciplinary > Library & information sciences > Automation of library & information processes Books > Computing & IT > Applications of computing > Databases > Data capture & analysis Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	0-387-95563-1
Barcode:	9780387955636

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