Introduction To Data Mining by Pang-Ning TanIntroduction To Data Mining by Pang-Ning Tan

Introduction To Data Mining

byPang-Ning Tan, Michael Steinbach, Anuj Karpatne

Hardcover | January 4, 2018

Pricing and Purchase Info

$148.80

Earn 744 plum® points

Prices and offers may vary in store

Out of stock online

Not available in stores

about

Introducing the fundamental concepts and algorithms of data mining

Introduction to Data Mining, 2nd Edition , gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of data mining to real problems. The text helps readers understand the nuances of the subject, and includes important sections on classification, association analysis, and cluster analysis. This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.

Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He received his M.S. degree in Physics and Ph.D. degree in Computer Science from University of Minnesota. His research interests focus on the development of novel data mining algorithms for a broad range of applications...
Loading
Title:Introduction To Data MiningFormat:HardcoverDimensions:864 pages, 9.2 × 7.4 × 1.3 inPublished:January 4, 2018Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0133128903

ISBN - 13:9780133128901

Reviews

Table of Contents

1. Introduction

2. Data

3. Classification: Basic Concepts and Techniques

4. Classification: Alternative Techniques

5. Association Analysis: Basic Concepts and Algorithms

6. Association Analysis: Advanced Concepts

7. Cluster Analysis: Basic Concepts and Algorithms

8. Cluster Analysis: Additional Issues and Algorithms

9. Anomaly Detection

10. Avoiding False Discoveries