Data Mining: A Hands On Approach for Business Professionals, 1/e Robert Groth, Danville, California Published September, 1997 by Prentice Hall Professional Technical Reference
Copyright 1998, 304 pp. Paper ISBN 0-13-756412-0 $42.00

See other books about Data Warehouse.

KEY BENEFIT:This book contains all the practical information, hands-on demos and software you need to understand data mining.

KEY TOPICS:This book doesn't just explain data mining concepts: it shows you exactly how to make the most of them. If you're in marketing, you'll learn how data mining can help you rank your customers by the likelihood they'll respond to your mailings. If you're in MIS, you'll learn exactly how to prepare relational data for data mining. You'll learn how to use each of three powerful data mining tools; demos for all three are included on CD-ROM. The book also includes detailed case studies for several of the industries that can benefit most from data mining, including banking, finance, retail, healthcare, direct marketing, and telecommunications. The book is replete with shortcuts and techniques that have never been published before.

MARKET:For all business and marketing professionals, systems analysts, database administrators, students and others who want to leverage the power of data mining.

For more information, see the book preface

• A market-focused, hands-on data mining guide for business professionals.
• Includes detailed case studies in retail, banking, health care and telecommunications.
• Contains powerful tools on CD-ROM: trial versions of DataMind, Angoss KnowledgeSEEKER, and NeuralWare Predict.

Series Foreword.

Foreword.

Preface.

Acknowledgments.

1. Introduction to Data Mining.

What is Data Mining?

Classification Studies (Supervised Learning). Clustering Studies (Unsupervised Learning). Visualization.

Why Use Data Mining? 1.3 How Do You Mine Data?

Data Preparation. Defining a Study. Reading Your Data and Building a Model. Understanding the Model. Prediction.

Data Mining Models.

Decision Trees. Genetic Algorithms. Neural Nets. Agent Network Technology. Hybrid Models. Statistics.

Data Mining Terminology. A Note on Privacy Issues. Summary.

2. The Data Mining Process.

The Example. Data Preparation.

Getting at Your Data. Data Qualification Issues. Data Quality Issues. Binning. Data Derivation.

Defining a Study.

Understanding Limits. Choosing a Good Study. Types of Studies. What Elements to Analyze? Issues of Sampling.

Reading the Data and Building a Model. Understanding Your Model. Prediction. Summary.

3. The Data Mining Marketplace.

Introduction (Trends). Data Mining Vendors. Visualization.

Examples of Data Visualization. Vendor List.

Useful Web Sites/Commercially Available Code.

Data Mining Web Sites. Finding Data Sets. Source Code.

Data Sources For Mining. Summary.

4. A Look at Angoss: KnowledgeSEEKER.

Introduction.

More on Decision Trees. How Decision Trees Are Being Used.

Data Preparation. Defining the Study. Building the Model. Understanding the Model.

Looking at Different Splits. Going to a Specific Split. Growing the Tree. Forcing a Split. Validation. Defining a New Scenario for a Study. Growing a Tree Automatically. Data Distribution.

Prediction. Summary.

5. A Look at DataMind.

Introduction.

More on Agent Network Technology. How DataMind is Being Used.

Data Preparation. Defining the Study. Read Your Data/Build a Discovery Model. Understanding the Model.

Model Summary Report. Scenario Summary Reports. Discovery Views. Microsoft Word Report. Evaluation.

Perform Prediction. Summary.

6. A Look at NeuralWorks Predict.

Introduction.

More on Neural Networks. How Corporate America is Using Neural Nets.

Data Preparation. Defining the Study.

Starting Up NeuralWorks Predict. Defining the New Study..

Building and Training the Model. Understanding the Model.

Validating the Model.

Prediction. Summary.

7. Industry Applications of Data Mining.

Data Mining Applications in Banking and Finance. Data Mining Applications in Retail. Data Mining Applications in Healthcare. Data Mining Applications in Telecommunications. Summary.

8. Enabling Data Mining Through Data Warehouses.

Introduction. A Data Warehouse Example in Banking and Finance.

The Example Data Model. An Example of a Credit Fraud Study. An Example of a Retention Management Study. Data Trends Analysis.

A Data Warehouse Example in Retail.

The Example Data Model. What Types of Customers are Buying Different Types of Products. An Example of Regional Studies and Others.

A Data Warehouse Example in Healthcare.

The Example Data Model. A Look at Example Studies in Healthcare. A Discussion on Adding Credit Data to Our Example.

A Data Warehouse Example in Telecommunications.

The Example Data Model. Data Collection. Creating the Data Set. An Example Study on Product/Market Share Analysis. An Example Study of a Regional Market Analysis.

Summary.

Appendix A. Data Mining Vendors.

Data Mining Players. Visualization Tools. Useful Web Sites. Information Access Providers. End User Query Vendors. EIS Players. Data Warehousing Vendors.

Appendix B. Installing Demo Software.

Installing Angoss KnowledgeSEEKER Demo.

Installing KnowledgeSEEKER for Windows 3.1. B.1.2 Installing KnowledgeSEEKER for Windows 95.

Installing the DataMind Professional Edition |Demo.

Installing DataMind for Windows 3.1. Installing DataMind for Windows 95.

Installing NeuralWorks Predict Demo.

Installing NeuralWorks Predict for Windows 3.1, Windows 3.11, or Windows NT 3.5.1. Installing NeuralWorks Predict for Windows 95 and Windows NT 4.0 and above. Copying a Sample Data File to Your Local Disk Drive. Getting Help.

Appendix C. References.

Index.

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