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Commercial Data Mining - 1st Edition - ISBN: 9780124166028, 9780124166585

Commercial Data Mining

1st Edition

Processing, Analysis and Modeling for Predictive Analytics Projects

Author: David Nettleton
Paperback ISBN: 9780124166028
eBook ISBN: 9780124166585
Imprint: Morgan Kaufmann
Published Date: 29th January 2014
Page Count: 304
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Table of Contents


Chapter 1: Introduction

Chapter 2: Business Objectives


Criteria for Choosing a Viable Project

Factors That Influence Project Benefits

Factors That Influence Project Costs

Example 1: Customer Call Center – Objective: IT Support for Customer Reclamations

Example 2: Online Music App – Objective: Determine Effectiveness of Advertising for Mobile Device Apps


Chapter 3: Incorporating Various Sources of Data and Information


Data about a Business’s Products and Services

Surveys and Questionnaires

Loyalty Card/Customer Card

Demographic Data

Macro-Economic Data

Data about Competitors

Financial Markets Data: Stocks, Shares, Commodities, and Investments

Chapter 4: Data Representation


Basic Data Representation

Advanced Data Representation

Chapter 5: Data Quality


Examples of Typical Data Problems

Relevance and Reliability

Quantitative Evaluation of the Data Quality

Data Extraction and Data Quality – Common Mistakes and How to Avoid Them

How Data Entry and Data Creation May Affect Data Quality

Chapter 6: Selection of Variables and Factor Derivation


Selection from the Available Data

Reverse Engineering: Selection by Considering the Desired Result

Data Mining Approaches to Selecting Variables

Packaged Solutions: Preselecting Specific Variables for a Given Business Sector


Chapter 7: Data Sampling and Partitioning


Sampling for Data Reduction

Partitioning the Data Based on Business Criteria

Issues Related to Sampling

Chapter 8: Data Analysis




Clustering and Segmentation

Segmentation and Visualization

Analysis of Transactional Sequences

Analysis of Time Series

Typical Mistakes when Performing Data Analysis and Interpreting Results

Chapter 9: Data Modeling


Modeling Concepts and Issues

Neural Networks

Classification: Rule/Tree Induction

Traditional Statistical Models

Other Methods and Techniques for Creating Predictive Models

Applying the Models to the Data

Simulation Models – “What If?”

Summary of Modeling

Chapter 10: Deployment Systems: From Query Reporting to EIS and Expert Systems


Query and Report Generation

Executive Information Systems

Expert Systems

Case-Based Systems


Chapter 11: Text Analysis

Basic Analysis of Textual Information

Advanced Analysis of Textual Information

Commercial Text Mining Products

Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses


Data Warehouse and Data Marts

Creating a File or Table for Data Mining

Chapter 13: CRM – Customer Relationship Management and Analysis


CRM Metrics and Data Collection

Customer Life Cycle

Example: Retail Bank

Integrated CRM Systems

Customer Satisfaction

Example CRM Application

Chapter 14: Analysis of Data on the Internet I – Website Analysis and Internet Search


Analysis of Trails left by Visitors to a Website

Search and Synthesis of Market Sentiment Information on the Internet


Chapter 15: Analysis of Data on the Internet II – Search Experience Analysis


The Internet and Internet Search

Data Mining of a User Search Log


Chapter 16: Analysis of Data on the Internet III – Online Social Network Analysis


Analysis of Online Social Network Graphs

Applications and Tools for Social Network Analysis


Chapter 17: Analysis of Data on the Internet IV – Search Trend Analysis over Time


Analysis of Search Term Trends Over Time

Data Mining Applied to Trend Data


Chapter 18: Data Privacy and Privacy-Preserving Data Publishing


Popular Applications and Data Privacy

Legal Aspects – Responsibility and Limits

Privacy-Preserving Data Publishing

Chapter 19: Creating an Environment for Commercial Data Analysis


Integrated Commercial Data Analysis Tools

Creating an Ad Hoc/Low-Cost Environment for Commercial Data Analysis

Chapter 20: Summary

Appendix: Case Studies

Case Study 1: Customer Loyalty at an Insurance Company

Case Study 2: Cross-Selling a Pension Plan at a Retail Bank

Case Study 3: Audience Prediction for a Television Channel





Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.

Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book.

Key Features

  • Illustrates cost-benefit evaluation of potential projects
  • Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools
  • Approachable reference can be read from cover to cover by readers of all experience levels
  • Includes practical examples and case studies as well as actionable business insights from author's own experience


Data mining professionals in business & IT.


No. of pages:
© Morgan Kaufmann 2014
29th January 2014
Morgan Kaufmann
Paperback ISBN:
eBook ISBN:


"...a mandatory volume for anyone who runs data mining projects, since all the steps and most important details that should not be forgotten are described here...I strongly recommend this book for anyone even slightly involved with data mining projects."--IEEE Communications Magazine, Commercial Data Mining

"I strongly disagree with Bellin’s statement that the book will not help practitioners, and one can only conclude that the reviewer is not familiar with what data mining practitioners need." -Computing Reviews, Oct 13, 2014

Ratings and Reviews

About the Author

David Nettleton

David F. Nettleton has more than 25 years of experience in IT system development, specializing in databases and data analysis. He has a Bachelor of Science degree in Computer Science, Master of Science degree in Computer Software and Systems Design and a Ph.D. in Artificial Intelligence. He has worked for IBM as a Business Intelligence Consultant, among other companies. In 1995 he founded his own consultancy dedicated to commercial data analysis projects, working in the Banking, Insurance, Media, Industry and Health Sectors. He has published over 40 articles and papers in journals, national and international congresses and magazines, and has given many presentations in conferences and workshops. He is currently a contract researcher at the Universitat Pompeu Fabra, Barcelona, Spain and at the IIIA-CSIC, Spain, specializing in data mining applied to online social networks and data privacy. Dr. Nettleton was born in England and lives in Barcelona, Spain since 1988.

Affiliations and Expertise

Contract Researcher at the Universitat Pompeu Fabra, Barcelona, Spain and at the IIIA-CSIC, Spain