Commercial Data Mining, 1st Edition

  • Published By:
  • ISBN-10: 012416658X
  • ISBN-13: 9780124166585
  • DDC: 658.056312
  • Grade Level Range: College Freshman - College Senior
  • 304 Pages | eBook
  • Original Copyright 2014 | Published/Released May 2014
  • This publication's content originally published in print form: 2014

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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Introduction.
2: Business Objectives.
3: Incorporating Various Sources of Data and Information.
4: Data Representation.
5: Data Quality.
6: Selection of Variables and Factor Derivation.
7: Data Sampling and Partitioning.
8: Data Analysis.
9: Data Modeling.
10: Deployment Systems: From Query Reporting to EIS and Expert Systems.
11: Text Analysis.
12: Data Mining from Relationally Structured Data, Marts, and Warehouses.
13: CRM – Customer Relationship Management and Analysis.
14: Analysis of Data on the Internet I – Website Analysis and Internet Search.
15: Analysis of Data on the Internet II – Search Experience Analysis.
16: Analysis of Data on the Internet III – Online Social Network Analysis.
17: Analysis of Data on the Internet IV – Search Trend Analysis Over Time.
18: Data Privacy and Privacy-Preserving Data Publishing.
19: Creating an Environment for Commercial Data Analysis.
20: Summary.
Appendix: Case Studies.