Introduction to Data Mining. Este artículo no está disponible.
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Idioma: inglés
Editorial: PEARSON INDIA (edition First Edition), 2005
- Primera edición
- Tapa blanda
- Usado

Librería: BooksRun, Philadelphia, PA, Estados Unidos de AmericaBooksRun
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Condición: Usado - Regular
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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.
N° de ref. del artículo 9332571406-7-1-7
- Título
- Introduction to Data Mining
- Autor
- Tan, Kumar, Steinbach
- Editorial
- PEARSON INDIA (edition First Edition)
- Año de publicación
- 2005
- Estado
- Fair
- Encuadernación
- Paperback
- Idioma
- inglés
- ISBN 10
- 0321321367
- ISBN 13
- 9780321321367
- Edición
- First Edition.
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. 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.
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Reseña del editor
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
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