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Herausgeber: 
  • I-Hsien Ting
  • Hui-Ju Wu
  • Tien-Hwa Ho
  • Mining and Analyzing Social Networks 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 4-7 Tagen versandfertig
    Veröffentlichung:  Mai 2010  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Artificial Intelligence / B / Computational Intelligence / engineering
    ISBN:  9783642134210 
    EAN-Code: 
    9783642134210 
    Verlag:  Springer Berlin Heidelberg 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #288 - Studies in Computational Intelligence  
    Dimensionen:  H 241 mm / B 160 mm / D 17 mm 
    Gewicht:  501 gr 
    Seiten:  196 
    Zus. Info:  HC runder Rücken kaschiert 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    Mining social networks has now becoming a very popular research area not only for data mining and web mining but also social network analysis. Data mining is a technique that has the ability to process and analyze large amount of data and by this to discover valuable information from the data. In recent year, due to the growth of social communications and social networking websites, data mining becomes a very important and powerful technique to process and analyze such large amount of data. Thus, this book will focus upon Mining and Analyzing social network. Some chapters in this book are extended from the papers that presented in MSNDS2009 (the First International Workshop on Mining Social Networks for Decision Support) and SNMABA2009 ((The International Workshop on Social Networks Mining and Analysis for Business Applications)). In addition, we also sent invitations to researchers that are famous in this research area to contribute for this book. The chapters of this book are introduced as follows: In chapter 1-Graph Model for Pattern Recognition in Text, Qin Wu et al. present a novel approach that uses a weighted directed multigraph for text pattern recognition. In the proposed methodology, a weighted directed multigraph model has been set up by using the distances between the keywords as the weights of arcs as well a keyword-frequency distance based algorithm has also been introduced. Case studies are also included in this chapter to show the performance is better than traditional means.
      



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