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008			150903s2005 xxu| o |||| 0|eng d
020			_a9780387345550 _99780387345550
024	7		_a10.1007/9780387345550 _2doi
035			_avtls000331168
039		9	_a201509030229 _bVLOAD _c201404121755 _dVLOAD _c201404091530 _dVLOAD _c201401311405 _dstaff _y201401301203 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.D343
100	1		_aWeiss, Sholom M. _eautor _9300849
245	1	0	_aText Mining : _bPredictive Methods for Analyzing Unstructured Information / _cby Sholom M. Weiss, Nitin Indurkhya, Tong Zhang, Fred J. Damerau.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_axii, 236 páginas, _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aOverview of Text Mining -- From Textual Information to Numerical Vectors -- Using Text for Prediction -- Information Retrieval and Text Mining -- Finding Structure in a Document Collection -- Looking for Information in Documents -- Case Studies -- Emerging Directions.
520			_aOne consequence of the pervasive use of computers is that most documents originate in digital form. Text mining—the process of searching, retrieving, and analyzing unstructured, natural-language text—is concerned with how to exploit the textual data embedded in these documents. Text Mining presents a comprehensive introduction and overview of the field, integrating related topics (such as artificial intelligence and knowledge discovery and data mining) and providing practical advice on how readers can use text-mining methods to analyze their own data. Emphasizing predictive methods, the book unifies all key areas in text mining: preprocessing, text categorization, information search and retrieval, clustering of documents, and information extraction. In addition, it identifies emerging directions for those looking to do research in the area. Some background in data mining is beneficial, but not essential. Topics and features: * Presents a comprehensive and easy-to-read introduction to text mining * Explores the application and utility of the methods, as well as the optimal techniques for specific scenarios * Provides several descriptive case studies that take readers from problem description to system deployment in the real world * Uses methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) * Includes access to downloadable software (runs on any computer), as well as useful chapter-ending historical and bibliographical remarks, a detailed bibliography, and subject and author indexes This authoritative and highly accessible text, written by a team of authorities on text mining, develops the foundation concepts, principles, and methods needed to expand beyond structured, numeric data to automated mining of text samples. Researchers, computer scientists, and advanced undergraduates and graduates with work and interests in data mining, machine learning, databases, and computational linguistics will find the work an essential resource.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aIndurkhya, Nitin. _eautor _9300850
700	1		_aZhang, Tong. _eautor _9300851
700	1		_aDamerau, Fred J. _eautor _9300852
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387954332
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-34555-0 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c277779 _d277779