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Autor(en): 
  • Ye Nong
  • Data Mining: Theories, Algorithms, and Examples 
     

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


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  März 2017  
    Genre:  Wirtschaft / Recht 
     
    advanced data mining methodologies / Bayesian Inference / Bayesian Network / Binary Decision Tree / COMPUTERS / Data Science / Data Analytics / Conditional Probability Distributions / Conditional Probability Table / Control Charts
    ISBN:  9781138073661 
    EAN-Code: 
    9781138073661 
    Verlag:  Taylor and Francis 
    Einband:  Kartoniert  
    Sprache:  English  
    Serie:  Data-Enabled Engineering  
    Dimensionen:  H 234 mm / B 156 mm / D  
    Gewicht:  498 gr 
    Seiten:  349 
    Illustration:  schwarz-weiss Illustrationen, Tabellen, schwarz-weiss 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    New technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various data mining fields. The book reviews theoretical rationales and procedural details of data mining algorithms, including those commonly found in the literature and those presenting considerable difficulty, using small data examples to explain and walk through the algorithms. The book covers a wide range of data mining algorithms, including those commonly found in data mining literature and those not fully covered in most of existing literature due to their considerable difficulty. The book presents a list of software packages that support the data mining algorithms, applications of the data mining algorithms with references, and exercises, along with the solutions manual and PowerPoint slides of lectures. The author takes a practical approach to data mining algorithms so that the data patterns produced can be fully interpreted. This approach enables students to understand theoretical and operational aspects of data mining algorithms and to manually execute the algorithms for a thorough understanding of the data patterns produced by them.

      



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