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008			150903s2008 xxk| o |||| 0|eng d
020			_a9781848000469 _99781848000469
024	7		_a10.1007/9781848000469 _2doi
035			_avtls000344136
039		9	_a201509030407 _bVLOAD _c201405050303 _dVLOAD _y201402061249 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA76.9.T48
100	1		_aBerry, Michael W. _eeditor. _9316359
245	1	0	_aSurvey of Text Mining II : _bClustering, Classification, and Retrieval / _cedited by Michael W. Berry, Malu Castellanos.
264		1	_aLondon : _bSpringer London, _c2008.
300			_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		_aClustering -- Cluster-Preserving Dimension Reduction Methods for Document Classification -- Automatic Discovery of SimilarWords -- Principal Direction Divisive Partitioning with Kernels and k-Means Steering -- Hybrid Clustering with Divergences -- Text Clustering with Local Semantic Kernels -- Document Retrieval and Representation -- Vector Space Models for Search and Cluster Mining -- Applications of Semidefinite Programming in XML Document Classification -- Email Surveillance and Filtering -- Discussion Tracking in Enron Email Using PARAFAC -- Spam Filtering Based on Latent Semantic Indexing -- Anomaly Detection -- A Probabilistic Model for Fast and Confident Categorization of Textual Documents -- Anomaly Detection Using Nonnegative Matrix Factorization -- Document Representation and Quality of Text: An Analysis.
520			_aThe proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry. This second volume continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining. Features: • Acts as an important benchmark in the development of current and future approaches to mining textual information • Serves as an excellent companion text for courses in text and data mining, information retrieval and computational statistics • Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems • Presents an overview of current methods and software for text mining • Highlights open research questions in document categorization and clustering, and trend detection • Describes new application problems in areas such as email surveillance and anomaly detection Survey of Text Mining II offers a broad selection in state-of-the art algorithms and software for text mining from both academic and industrial perspectives, to generate interest and insight into the state of the field. This book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining. Michael W. Berry is a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Malu Castellanos is a senior researcher at Hewlett-Packard Laboratories in Palo Alto, California.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aCastellanos, Malu. _eeditor. _9323582
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781848000452
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84800-046-9 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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