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Commercial Data Mining
Processing, Analysis and Modeling for Predictive Analytics Projects
This approachable guide will help you avoid costly mistakes when embarking on your commercial data mining project.
David Nettleton (Author)
9780124166028, Elsevier Science
Paperback, published 8 April 2014
304 pages, 65 illustrations
22.9 x 15.1 x 2 cm, 0.5 kg
"...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
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.
1. Introduction2. Business Objectives3. Data Quality4. Data Representation5. Possible Sources of Data and Information6. Selection of Variables and Factors7. Data Sampling8. Data Analysis9. Modeling10. The Data Mart – Structured Data Warehouse11. Querying, Report Generation and Executive Information Systems12. Analytical CRM – Customer Relationship Analysis13. Website Analysis and Internet Search14. Online Social Network Analysis15. Web Search Trend Analysis16. Creating your own Environment for Commercial Data Analysis17. SummaryAppendices, Case Studies
Subject Areas: Data mining [UNF], Database programming [UMT]
