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

Format:

Book

Author/Creator:

Nettleton, David.

Series:

The Savvy Manager's Guides

Language:

English

Subjects (All):

Management--Mathematical models.

Management -- Mathematical models.

Physical Description:

1 online resource (361 pages)

Edition:

1st ed.

Place of Publication:

San Diego : Elsevier Science & Technology, 2014.

Contents:

Front Cover

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

Copyright

Contents

Acknowledgments

Chapter 1: Introduction

Chapter 2: Business Objectives

Introduction

Criteria for Choosing a Viable Project

Evaluation of Potential Commercial Data Analysis Projects - General Considerations

Evaluation of Viability in Terms of Available Data - Specific Considerations

Factors That Influence Project Benefits

Factors That Influence Project Costs

Example1: Customer Call Center - Objective: IT Support for Customer Reclamations

Overall Evaluation of the Cost and Benefit of Mr. Strongs Project

Example2: Online Music App - Objective: Determine Effectiveness of Advertising for Mobile Device Apps

Overall Evaluation of the Cost and Benefit of Melody-onlines Project

Summary

Further Reading

Chapter 3: Incorporating Various Sources of Data and Information

Data about a Businesss Products and Services

Surveys and Questionnaires

Examples of Survey and Questionnaire Forms

Surveys and Questionnaires: Data Table Population

Issues When Designing Forms

Loyalty Card/Customer Card

Registration Form for a Customer Card

Customer Card Registrations: Data Table Population

Transactional Analysis of Customer Card Usage

Demographic Data

The Census: Census Data, United States, 2010

Macro-Economic Data

Data about Competitors

Financial Markets Data: Stocks, Shares, Commodities, and Investments

Chapter 4: Data Representation

Basic Data Representation

Basic Data Types

Representation, Comparison, and Processing of Variables of Different Types

Principal Types of Variables

Normalization of the Values of a Variable

Distribution of the Values of a Variable

Atypical Values - Outliers.

Advanced Data Representation

Hierarchical Data

Semantic Networks

Graph Data

Fuzzy Data

Chapter 5: Data Quality

Examples of Typical Data Problems

Content Errors in the Data

Relevance and Reliability

Quantitative Evaluation of the Data Quality

Data Extraction and Data Quality - Common Mistakes and How to Avoid Them

Data Extraction

Data Validation Filters

Derived Data

Summary of Data Extraction Example

How Data Entry and Data Creation May Affect Data Quality

Chapter 6: Selection of Variables and Factor Derivation

Selection from the Available Data

Statistical Techniques for Evaluating a Set of Input Variables

Correlation

Factorial Analysis

Data Fusion

Summary of the Approach of Selecting from the Available Data

Reverse Engineering: Selection by Considering the Desired Result

Statistical Techniques for Evaluating and Selecting Input Variables for a Specific Business Objective

Transforming Numerical Variables into Ordinal Categorical Variables

Customer Segmentation

Variable Selection - Reverse Engineering

Final Segmentation Model

Summary of the Reverse Engineering Approach

Data Mining Approaches to Selecting Variables

Rule Induction

Neural Networks

Clustering

Packaged Solutions: Preselecting Specific Variables for a Given Business Sector

The FAMS (Fraud and Abuse Management) System

Chapter 7: Data Sampling and Partitioning

Sampling for Data Reduction

Partitioning the Data Based on Business Criteria

Issues Related to Sampling

Sampling versus Big Data

Chapter 8: Data Analysis

Visualization

Associations

Clustering and Segmentation

Segmentation and Visualization

Analysis of Transactional Sequences

Analysis of Time Series.

Bank Current Account: Time Series Data Profiles

Typical Mistakes when Performing Data Analysis and Interpreting Results

Chapter 9: Data Modeling

Modeling Concepts and Issues

Supervised and Unsupervised Learning

Cross-Validation

Evaluating the Results of Data Models - Measuring Precision

Predictive Neural Networks

Kohonen Neural Network for Clustering

Classification: Rule/Tree Induction

The ID3 Decision Tree Induction Algorithm

The C4.5 Decision Tree Induction Algorithm

The C5.0 Decision Tree Induction Algorithm

Traditional Statistical Models

Regression Techniques

Summary of the use of regression techniques

K-means

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

Query and Report Generation

Query and Reporting Systems

Executive Information Systems

EIS Interface for a ``What If´´ Scenario Modeler

Executive Information Systems (EIS)

Expert Systems

Case-Based Systems

Chapter 11: Text Analysis

Basic Analysis of Textual Information

Advanced Analysis of Textual Information

Keyword Definition and Information Retrieval

Identification of Names and Personal Information of Individuals

Identifying Blocks of Interesting Text

Information Retrieval Concepts

Assessing Sentiment on Social Media

Commercial Text Mining Products

Chapter 12: Data Mining from Relationally Structured Data, Marts, and Warehouses

Data Warehouse and Data Marts

Creating a File or Table for Data Mining

Chapter 13: CRM - Customer Relationship Management and Analysis

CRM Metrics and Data Collection

Customer Life Cycle

Example: Retail Bank.

Integrated CRM Systems

CRM Application Software

Customer Satisfaction

Example CRM Application

Chapter 14: Analysis of Data on the Internet I - Website Analysis and Internet Search

Chapter e14: Analysis of Data on the Internet I - Website Analysis and Internet Search

Analysis of Trails left by Visitors to a Website

Cookies - Tracking User Activity and Storing Information

Software for Web Analytics

Search and Synthesis of Market Sentiment Information on the Internet

Automatic Web Crawlers and Web Scrapers

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

Chapter e15: Analysis of Data on the Internet II - Search Experience Analysis

The Internet and Internet Search

The Structure of the Web and How Search Engines Rank Results

Types of Internet Searches

Data Mining of a User Search Log

Representing User Search Behavior: Query Sessions

Defining the Quality of the User Search Experience

Data Mining of User Search Experience Data

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

Chapter e16: Analysis of Data on the Internet III - Online Social Network Analysis

Analysis of Online Social Network Graphs

Graph Metrics

Input Formats for Graph Data

The .csv Plain Text File

GML (Graph Modeling Language) Format

GraphML Format

Visualization and Interpretation of Graphs

Applications and Tools for Social Network Analysis

Commercial Software

Open Source and Freeware

Obtaining OSN Data

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

Chapter e17: Analysis of Data on the Internet IV - Search Trend Analysis over Time

Analysis of Search Term Trends over Time.

Google Trends - Relating Trend Patterns to Specific Tendencies

Tendency Type S: Steady Increase/Decrease

Tendency Types C: Cyclical and S: Steady Increase/Decrease

Tendency Types B: Ad Hoc Bursts and C: Cyclical

Tendency Type P: One-Time Peak

Data Mining Applied to Trend Data

Derived Factors Used to Represent Trends

Data Extraction and Preprocessing

Clustering and Predictive Modeling of the Trends

Using k-Means Clustering

Predictive Modeling

Chapter 18: Data Privacy and Privacy-Preserving Data Publishing

Popular Applications and Data Privacy

Legal Aspects - Responsibility and Limits

Privacy-Preserving Data Publishing

Privacy Concepts

Anonymity

Information Loss

Risk of Disclosure

Anonymization Techniques

Document Sanitization

Chapter 19: Creating an Environment for Commercial Data Analysis

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

Definition of the Operational and Informational Data of Interest

Data Extraction and Creation of Files for Analysis

Data Exploration

Modeling Phase

Deployment: Using the Information and the Production Version

Examples of Rules for Clients Who Cancel

Examples of Rules for the Most Loyal Clients

Case Study 2: Cross-Selling a Pension Plan at a Retail Bank

Data Definition

Internal Data

External Data

Output of the Data Model

Data Analysis

Definition of New Derived variables and Filtering of Variables with Lowest Relevance

Model Generation

Clustering and Profile Identification

Predictive Models

Results and Conclusions.

Example Weka Screens: Data Processing, Analysis, and Modeling.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Nettleton, David Commercial Data Mining

ISBN:

9780124166585

OCLC:

880900252