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005 | 20160429155738.0 | ||
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008 | 150903s2011 gw | o |||| 0|eng d | ||
020 |
_a9783642194603 _99783642194603 |
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024 | 7 |
_a10.1007/9783642194603 _2doi |
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035 | _avtls000356616 | ||
039 | 9 |
_a201509030538 _bVLOAD _c201405060407 _dVLOAD _y201402191223 _zstaff |
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_aMX-SnUAN _bspa _cMX-SnUAN _erda |
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050 | 4 | _aQA75.5-76.95 | |
100 | 1 |
_aLiu, Bing. _eautor _9331198 |
|
245 | 1 | 0 |
_aWeb Data Mining : _bExploring Hyperlinks, Contents, and Usage Data / _cby Bing Liu. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011. |
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300 |
_axx, 624 páginas _brecurso en línea. |
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336 |
_atexto _btxt _2rdacontent |
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337 |
_acomputadora _bc _2rdamedia |
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338 |
_arecurso en línea _bcr _2rdacarrier |
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347 |
_aarchivo de texto _bPDF _2rda |
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490 | 0 | _aData-Centric Systems and Applications | |
500 | _aSpringer eBooks | ||
505 | 0 | _a1. Introduction -- Part I: Data Mining Foundations -- 2. Association Rules and Sequential Patterns -- 3. Supervised Learning -- 4. Unsupervised Learning -- 5. Partially Supervised Learning -- Part II: Web Mining -- 6. Information Retrieval and Web Search -- 7. Social Network Analysis -- 8. Web Crawling -- 9. Structured Data Extraction: Wrapper Generation -- 10. Information Integration -- 11. Opinion Mining and Sentiment Analysis -- 12. Web Usage Mining. | |
520 | _aWeb mining aims to discover useful information and knowledge from Web hyperlinks, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi-structured and unstructured nature of the Web data. The field has also developed many of its own algorithms and techniques. Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online. | ||
590 | _aPara consulta fuera de la UANL se requiere clave de acceso remoto. | ||
710 | 2 |
_aSpringerLink (Servicio en línea) _9299170 |
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776 | 0 | 8 |
_iEdición impresa: _z9783642194597 |
856 | 4 | 0 |
_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-642-19460-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL) |
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