000 03400nam a22003735i 4500
001 302211
003 MX-SnUAN
005 20160429155738.0
007 cr nn 008mamaa
008 150903s2011 gw | o |||| 0|eng d
020 _a9783642194603
_99783642194603
024 7 _a10.1007/9783642194603
_2doi
035 _avtls000356616
039 9 _a201509030538
_bVLOAD
_c201405060407
_dVLOAD
_y201402191223
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
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.
300 _axx, 624 páginas
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
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
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)
942 _c14
999 _c302211
_d302211