Introduction to Data Mining

Tan, Kumar, Steinbach

ISBN 10: 0321321367 / ISBN 13: 9780321321367

Editore: PEARSON INDIA (edition First Edition), 2005

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The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way. International edition.

Codice articolo 9332571406-7-1-7

Dati bibliografici

• Titolo: Introduction to Data Mining
• Editore: PEARSON INDIA (edition First Edition)
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 0321321367
• ISBN 13: 9780321321367
• Rilegatura: Paperback
• Edizione: First Edition.
• Condizione: Fair

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