Introduction to Data Mining, First Edition, Pearson New International Edition

Pang-Ning Tan; Michael Steinbach; Vipin Kumar

ISBN 10: 1292026154 / ISBN 13: 9781292026152

Published by Pearson Education, Harlow, Essex, England, 2014

About this Item

Pearson Education, 2014. Trade Paperback. Fine. Interior pristine. Spine straight, tight and uncreased. Light corner wear. Not from a library. No remainder mark. 732 pages. 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.

Seller Inventory # RWARE0000003479

Bibliographic Details

• Title: Introduction to Data Mining, First Edition, Pearson New International Edition
• Publisher: Pearson Education, Harlow, Essex, England
• Publication Date: 2014
• Language: English
• ISBN 10: 1292026154
• ISBN 13: 9781292026152
• Binding: Trade Paperback
• Edition: 1st Edition
• Condition: Fine

About this title

Synopsis

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