Book description
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.
- 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
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright page
- Acknowledgments
- Chapter 1: Introduction
-
Chapter 2: Business Objectives
- Abstract
- Introduction
- 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
- Summary
- Chapter 3: Incorporating Various Sources of Data and Information
- Chapter 4: Data Representation
- Chapter 5: Data Quality
- Chapter 6: Selection of Variables and Factor Derivation
- Chapter 7: Data Sampling and Partitioning
- Chapter 8: Data Analysis
- Chapter 9: Data Modeling
- Chapter 10: Deployment Systems: From Query Reporting to EIS and Expert Systems
- Chapter 11: Text Analysis
- Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses
- Chapter 13: CRM – Customer Relationship Management and Analysis
- Chapter 14: Analysis of Data on the Internet I – Website Analysis and Internet Search
- Chapter e14: Analysis of Data on the Internet I – Website Analysis and Internet Search
- Chapter 15: Analysis of Data on the Internet II – Search Experience Analysis
- Chapter e15: Analysis of Data on the Internet II – Search Experience Analysis
- Chapter 16: Analysis of Data on the Internet III – Online Social Network Analysis
- Chapter e16: Analysis of Data on the Internet III – Online Social Network Analysis
- Chapter 17: Analysis of Data on the Internet IV – Search Trend Analysis over Time
- Chapter e17: Analysis of Data on the Internet IV – Search Trend Analysis over Time
- Chapter 18: Data Privacy and Privacy-Preserving Data Publishing
- Chapter 19: Creating an Environment for Commercial Data Analysis
- Chapter 20: Summary
- Appendix: Case Studies
- Glossary
- Glossary
- Bibliography
- Index
Product information
- Title: Commercial Data Mining
- Author(s):
- Release date: January 2014
- Publisher(s): Morgan Kaufmann
- ISBN: 9780124166585
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