000 03775nam a22005295i 4500
001 978-3-642-19460-3
003 DE-He213
005 20140220083756.0
007 cr nn 008mamaa
008 110624s2011 gw | s |||| 0|eng d
020 _a9783642194603
_9978-3-642-19460-3
024 7 _a10.1007/978-3-642-19460-3
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
_2bicssc
072 7 _aUND
_2bicssc
072 7 _aCOM030000
_2bisacsh
082 0 4 _a025.04
_223
100 1 _aLiu, Bing.
_eauthor.
245 1 0 _aWeb Data Mining
_h[electronic resource] :
_bExploring Hyperlinks, Contents, and Usage Data /
_cby Bing Liu.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2011.
300 _aXX, 624 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aData-Centric Systems and Applications
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.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aArtificial intelligence.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aPattern Recognition.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642194597
830 0 _aData-Centric Systems and Applications
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-19460-3
912 _aZDB-2-SCS
999 _c107557
_d107557