Commercial data mining : processing, analysis and modeling for predictive analytics projects

Title:

Personal Author:

Nettleton, David, 1963-, author

Publication Information:

Amsterdam : Elsevier, 2014

Physical Description:

xi, 288 pages ; 23 cm.

ISBN:

9780124166028

Subject Term:

Data mining

Management -- Mathematical models

Management -- Data processing

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010337167	HD30.25 N48 2014	Open Access Book	Book	Searching... Unknown
Searching... PSZ KL	33000000016496	HD30.25 N48 2014	Open Access Book	Book	Searching... Unknown

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.

Author Notes

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.

Acknowledgments	p. xi
1 Introduction	p. 1
2 Business Objectives	p. 7
Introduction	p. 7
Criteria for Choosing a Viable Project	p. 8
Evaluation of Potential Commercial Data Analysis Projects - General Considerations	p. 8
Evaluation of Viability in Terms of Available Data - Specific Considerations	p. 8
Factors That Influence Project Benefits	p. 9
Factors That Influence Project Costs	p. 10
Example 1: Customer Call Center - Objective: IT Support for Customer Reclamations	p. 10
Overall Evaluation of the Cost and Benefit of Mr. Strong's Project	p. 12
Example 2: Online Music App - Objective: Determine Effectiveness of Advertising for Mobile Device Apps	p. 13
Overall Evaluation of the Cost and Benefit of Melody-online's Project	p. 14
Summary	p. 15
Further Reading	p. 16
3 Incorporating Various Sources of Data and Information	p. 17
Introduction	p. 17
Data about a Business's Products and Services	p. 19
Surveys and Questionnaires	p. 20
Examples of Survey and Questionnaire Forms	p. 21
Surveys and Questionnaires: Data Table Population	p. 24
Issues When Designing Forms	p. 24
Loyalty Card/Customer Card	p. 26
Registration Form for a Customer Card	p. 27
Customer Card Registrations: Data Table Population	p. 30
Transactional Analysis of Customer Card Usage	p. 36
Demographic Data	p. 38
The Census: Census Data, United States, 2010	p. 39
Macro-Economic Data	p. 40
Data about Competitors	p. 43
Financial Markets Data: Stocks, Shares, Commodities, and Investments	p. 45
4 Data Representation	p. 49
Introduction	p. 49
Basic Data Representation	p. 49
Basic Data Types	p. 49
Representation, Comparison, and Processing of Variables of Different Types	p. 51
Normalization of the Values of a Variable	p. 56
Distribution of the Values of a Variable	p. 57
Atypical Values - Outliers	p. 58
Advanced Data Representation	p. 61
Hierarchical Data	p. 61
Semantic Networks	p. 62
Graph Data	p. 63
Fuzzy Data	p. 64
5 Data Quality	p. 67
Introduction	p. 67
Examples of Typical Data Problems	p. 69
Content Errors in the Data	p. 70
Relevance and Reliability	p. 71
Quantitative Evaluation of the Data Quality	p. 73
Data Extraction and Data Quality - Common Mistakes and How to Avoid Them	p. 74
Data Extraction	p. 74
Derived Data	p. 77
Summary of Data Extraction Example t	p. 77
How Data Entry and Data Creation May Affect Data Quality	p. 78
6 Selection of Variables and Factor Derivation	p. 79
Introduction	p. 79
Selection from the Available Data	p. 80
Statistical Techniques for Evaluating a Set of Input Variables	p. 81
Summary of the Approach of Selecting from the Available Data	p. 87
Reverse Engineering: Selection by Considering the Desired Result	p. 87
Statistical Techniques for Evaluating and Selecting Input Variables For a Specific Business Objective	p. 87
Transforming Numerical Variables into Ordinal Categorical Variables	p. 90
Customer Segmentation	p. 92
Summary of the Reverse Engineering Approach	p. 99
Data Mining Approaches to Selecting Variables	p. 99
Rule Induction	p. 99
Neural Networks	p. 100
Clustering	p. 101
Packaged Solutions: Preselecting Specific Variables for a Given Business Sector	p. 101
The FAMS (Fraud and Abuse Management) System	p. 103
Summary	p. 104
7 Data Sampling and Partitioning	p. 105
Introduction	p. 105
Sampling for Data Reduction	p. 106
Partitioning the Data Based on Business Criteria	p. 111
Issues Related to Sampling	p. 115
Sampling versus Big Data	p. 116
8 Data Analysis	p. 119
Introduction	p. 119
Visualization	p. 120
Associations	p. 121
Clustering and Segmentation	p. 122
Segmentation and Visualization	p. 124
Analysis of Transactional Sequences	p. 129
Analysis of Time Series	p. 130
Bank Current Account: Time Series Data Profiles	p. 131
Typical Mistakes when Performing Data Analysis and Interpreting Results	p. 134
9 Data Modeling	p. 137
Introduction	p. 137
Modeling Concepts and Issues	p. 137
Supervised and Unsupervised Learning	p. 137
Cross-Validation	p. 138
Evaluating the Results of Data Models - Measuring Precision	p. 139
Neural Networks	p. 141
Predictive Neural Networks	p. 141
Kohonen Neural Network for Clustering	p. 144
Classification: Rule/Tree Induction	p. 144
The ID3 Decision Tree Induction Algorithm	p. 146
The C4.5 Decision Tree Induction Algorithm	p. 147
The C5.0 Decision Tree Induction Algorithm	p. 148
Traditional Statistical Models	p. 149
Regression Techniques	p. 149
Summary of the use of regression techniques	p. 151
K-means	p. 151
Other Methods and Techniques for Creating Predictive Models	p. 152
Applying the Models to the Data	p. 153
Simulation Models - "What If?"	p. 154
Summary of Modeling	p. 156
10 Deployment Systems: From Query Reporting to EIS and Expert Systems	p. 159
Introduction	p. 159
Query and Report Generation	p. 159
Query and Reporting Systems	p. 163
Executive Information Systems	p. 164
EIS Interface for a "What If" Scenario Modeler	p. 164
Executive Information Systems (EIS)	p. 166
Expert Systems	p. 167
Case-Based Systems	p. 169
Summary	p. 170
11 Text Analysis	p. 171
Basic Analysis of Textual Information	p. 171
Advanced Analysis of Textual Information	p. 172
Keyword Definition and Information Retrieval	p. 173
Identification of Names and Personal Information of Individuals	p. 173
Identifying Blocks of Interesting Text	p. 174
Information Retrieval Concepts	p. 175
Assessing Sentiment on Social Media	p. 176
Commercial Text Mining Products	p. 178
12 Data Mining from Relationally Structured Data, Marts, and Warehouses	p. 181
Introduction	p. 181
Data Warehouse and Data Marts	p. 182
Creating a File or Table for Data Mining	p. 186
13 CRM - Customer Relationship Management and Analysis	p. 195
Introduction	p. 195
CRM Metrics and Data Collection	p. 195
Customer Life Cycle	p. 196
Example: Retail Bank	p. 198
Integrated CRM Systems	p. 200
CRM Application Software	p. 200
Customer Satisfaction	p. 201
Example CRM Application	p. 201
14 Analysis of Data on the Internet I - Website Analysis and Internet Search (Online Chapter)	p. 209
15 Analysis of Data on the Internet II - Search Experience Analysis (Online Chapter)	p. 211
16 Analysis of Data on the Internet III - Online Social Network Analysis (Online Chapter)	p. 213
17 Analysis of Data on the Internet IV - Search Trend Analysis over Time (Online Chapter)	p. 215
18 Data Privacy and Privacy-Preserving Data Publishing	p. 217
Introduction	p. 217
Popular Applications and Data Privacy	p. 218
Legal Aspects - Responsibility and Limits	p. 220
Privacy-Preserving Data Publishing	p. 221
Privacy Concepts	p. 221
Anonymization Techniques	p. 223
Document Sanitization	p. 226
19 Creating an Environment for Commercial Data Analysis	p. 229
Introduction	p. 229
Integrated Commercial Data Analysis Tools	p. 229
Creating an Ad Hoc/Low-Cost Environment for Commercial Data Analysis	p. 233
20 Summary	p. 239
Appendix: Case Studies	p. 241
Case Study 1 Customer Loyalty at an Insurance Company	p. 241
Introduction	p. 241
Definition of the Operational and Informational Data of Interest	p. 242
Data Extraction and Creation of Files for Analysis	p. 242
Data Exploration	p. 243
Modeling Phase	p. 248
Case Study 2 Cross-Selling a Pension Plan at a Retail Bank	p. 251
Introduction	p. 252
Data Definition	p. 252
Data Analysis	p. 255
Model Generation	p. 259
Results and Conclusions	p. 262
Example Weka Screens: Data Processing, Analysis, and Modeling	p. 262
Case Study 3 Audience Prediction for a Television Channel	p. 268
Introduction	p. 268
Data Definition	p. 269
Data Analysis	p. 270
Audience Prediction by Program	p. 272
Audience Prediction for Publicity Blocks	p. 273
Glossary (Online)	p. 277
Bibliography	p. 279
Index	p. 281

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Author Notes

Table of Contents