Commercial Data Mining

Commercial Data Mining

Processing, Analysis and Modeling for Predictive Analytics Projects

1st Edition - January 29, 2014

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  • Author: David Nettleton
  • Paperback ISBN: 9780124166028
  • eBook ISBN: 9780124166585

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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.

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

Readership

Data mining professionals in business & IT.

Table of Contents

  • Acknowledgments

    Chapter 1: Introduction

    Chapter 2: Business Objectives

    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

    Introduction

    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

    Introduction

    Basic Data Representation

    Advanced Data Representation

    Chapter 5: Data Quality

    Introduction

    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

    Introduction

    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

    Summary

    Chapter 7: Data Sampling and Partitioning

    Introduction

    Sampling for Data Reduction

    Partitioning the Data Based on Business Criteria

    Issues Related to Sampling

    Chapter 8: Data Analysis

    Introduction

    Visualization

    Associations

    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

    Introduction

    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

    Introduction

    Query and Report Generation

    Executive Information Systems

    Expert Systems

    Case-Based Systems

    Summary

    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

    Introduction

    Data Warehouse and Data Marts

    Creating a File or Table for Data Mining

    Chapter 13: CRM – Customer Relationship Management and Analysis

    Introduction

    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

    Introduction

    Analysis of Trails left by Visitors to a Website

    Search and Synthesis of Market Sentiment Information on the Internet

    Summary

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

    Introduction

    The Internet and Internet Search

    Data Mining of a User Search Log

    Summary

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

    Introduction

    Analysis of Online Social Network Graphs

    Applications and Tools for Social Network Analysis

    Summary

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

    Introduction

    Analysis of Search Term Trends Over Time

    Data Mining Applied to Trend Data

    Summary

    Chapter 18: Data Privacy and Privacy-Preserving Data Publishing

    Introduction

    Popular Applications and Data Privacy

    Legal Aspects – Responsibility and Limits

    Privacy-Preserving Data Publishing

    Chapter 19: Creating an Environment for Commercial Data Analysis

    Introduction

    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

    Glossary

    Bibliography

    Index

Product details

  • No. of pages: 304
  • Language: English
  • Copyright: © Morgan Kaufmann 2014
  • Published: January 29, 2014
  • Imprint: Morgan Kaufmann
  • Paperback ISBN: 9780124166028
  • eBook ISBN: 9780124166585

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

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