Introduction to Data Mining; Pang-Ning Tan, Michael Steinbach, Vipin Kumar; 2005
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Introduction to Data Mining Upplaga 1

av Pang-Ning Tan, Michael Steinbach, Vipin Kumar
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
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
Upplaga: 1a upplagan
Utgiven: 2005
ISBN: 9780321321367
Förlag: Pearson
Format: Inbunden
Språk: Engelska
Sidor: 792 st
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
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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