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Practical Applications of Data Mining

Author(s): Sang C. Suh, PhD, Professor, Texas A& M University, Commerce, Texas
  • ISBN-13: 9780763785871
  • Paperback    420 pages      © 2012
Price: $152.95 US List
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Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems.

Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and rough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowledge discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.

Features & Benefits

  • Offers an introduction to practical applications of data mining algorithms with clear illustrations of concepts and techniques
  • Contains a rich set of examples in each chapter to connect theories to practices
  • Covers topics needed to meet the requirements of modern data and knowledge engineering processes
  • Enhances student learning with online access to data mining algorithm implementation

Applicable Courses

Ideal for course in data mining and applying data mining techniques



Chapter 1 Introduction to Data Mining

Chapter 2 Association Rules

Chapter 3 Classification Learning

Chapter 4 Statistics for Data Mining

Chapter 5 Rough Sets and Bayes Theories

Chapter 6 Neural Networks

Chapter 7 Clustering

Chapter 8 Fuzzy Information Retrieval

Sang C. Suh, PhD-Professor, Texas A& M University, Commerce, Texas

  • Suh (Texas A&M, Commerce) focuses on the implementation of practical mining techniques that can be applied in real-life situations. The author often specifies approaches and methods either in database language or in pseudocode formats. Many of the examples are tabular in nature, and are well suited for working within the framework of a database. Hence, the material is most beneficial to those with prior college-level training in database design. Separate chapters cover popular data mining methods; they include "Association Rules," "Classification Learning," "Rough Sets and Bayes Theories," "Neural Networks," "Clustering," and "Fuzzy Information Retrieval." Ample figures illustrate the underlying concepts covered. A particularly valuable feature of this book is the inclusion of an extensive bibliography at the end of each chapter with notes from the author on selected references. This reference list can be an excellent guide to those interested in further exploration of the presented material.

    - J. Y. Cheung, emeritus, University of Oklahoma

The following instructor resources are available to qualified instructors for download

ISBN-13: 9780763785871

Slides in PowerPoint Format
Solutions Manual

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