Introduction to Data Mining

Pang-Ning Tan; Michael Steinbach; Vipin Kumar

ISBN 10: 0321321367 / ISBN 13: 9780321321367

Editore: Pearson, 2005

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Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.94.

Codice articolo G0321321367I3N10

Dati bibliografici

• Titolo: Introduction to Data Mining
• Editore: Pearson
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 0321321367
• ISBN 13: 9780321321367
• Rilegatura: Hardcover
• Condizione: Good
• Condizione della sovraccoperta: No Jacket

Descrizione articolo

Riassunto

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics.  

Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

 

 

Quotes

This book provides a comprehensive coverage of important data mining techniques. Numerous examples are provided to lucidly illustrate the key concepts.

-Sanjay Ranka, University of Florida

 

In my opinion this is currently the best data mining text book on the market. I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining (association rules).

-Mohammed Zaki, Rensselaer Polytechnic Institute

Product Description

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Material from related fields such as statistics and linear algebra, is integrated directly into the text. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Theoretical and practical coverage of all data mining topics Large number of integrated examples and figures Instructor Resources including solutions for most exercises and complete set of lecture slides (PowerPoint format). Minimal prerequisites-assume only a modest statistics or mathematics background, and no database knowledge is needed Predictive modeling Association analysis Clustering Anomaly detection Visualization