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Autor(en): 
  • Bing Liu
  • Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Juni 2011  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / Automated Pattern Recognition / B / computer science / Data Mining / Data Mining and Knowledge Discovery / Data Warehousing / Expert systems / knowledge-based systems
    ISBN:  9783642194597 
    EAN-Code: 
    9783642194597 
    Verlag:  Springer EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  Data-Centric Systems and Applications  
    Dimensionen:  H 235 mm / B 155 mm / D 41 mm 
    Gewicht:  1092 gr 
    Seiten:  624 
    Illustration:  XX, 624 p. 
    Zus. Info:  EUDR exemption - product or manufacturing materials placed on the market prior to 31.12.2025. 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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.

      



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