Commercial Data Mining
Processing, Analysis and Modeling for Predictive Analytics Projects
Description
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