Commercial Data Mining

1st Edition

Processing, Analysis and Modeling for Predictive Analytics Projects

Print ISBN: 9780124166028
eBook ISBN: 9780124166585
Imprint: Morgan Kaufmann
Published Date: 19th February 2014
Page Count: 304
38.95 + applicable tax
30.99 + applicable tax
49.95 + applicable tax
Compatible Not compatible
VitalSource PC, Mac, iPhone & iPad Amazon Kindle eReader
ePub & PDF Apple & PC desktop. Mobile devices (Apple & Android) Amazon Kindle eReader
Mobi Amazon Kindle eReader Anything else

Institutional Access


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.

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


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