Book description
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Table of contents
- Cover
- Title page
- Copyright page
- Glossary of terms
-
Part I: Data mining concept
- 1 Introduction
-
2 Data mining definition
- 2.1 Types of Data Mining Questions
- 2.2 Data Mining Process
- 2.3 Business Task: Clarification of the Business Question behind the Problem
- 2.4 Data: Provision and Processing of the Required Data
- 2.5 Modelling: Analysis of the Data
- 2.6 Evaluation and Validation during the Analysis Stage
- 2.7 Application of Data Mining Results and Learning from the Experience
-
Part II: Data mining Practicalities
-
3 All about data
- 3.1 Some Basics
- 3.2 Data Partition: Random Samples for Training, Testing and Validation
- 3.3 Types of Business Information Systems
- 3.4 Data Warehouses
- 3.5 Three Components of a Data Warehouse: DBMS, DB and DBCS
- 3.6 Data Marts
- 3.7 A Typical Example from the Online Marketing Area
- 3.8 Unique Data Marts
- 3.9 Data Mart: Do’s and Don’ts
-
4 Data Preparation
- 4.1 Necessity of Data Preparation
- 4.2 From Small and Long to Short and Wide
- 4.3 Transformation of Variables
- 4.4 Missing Data and Imputation Strategies
- 4.5 Outliers
- 4.6 Dealing with the Vagaries of Data
- 4.7 Adjusting the Data Distributions
- 4.8 Binning
- 4.9 Timing Considerations
- 4.10 Operational Issues
- 5 Analytics
-
6 Methods
- 6.1 Methods Overview
- 6.2 Supervised Learning
- 6.3 Multiple Linear Regression for Use When Target is Continuous
- 6.4 Regression When the Target is Not Continuous
- 6.5 Decision Trees
- 6.6 Neural Networks
- 6.7 Which Method Produces the Best Model? A Comparison of Regression, Decision Trees and Neural Networks
- 6.8 Unsupervised Learning
- 6.9 Cluster Analysis
- 6.10 Kohonen Networks and Self-Organising Maps
- 6.11 Group Purchase Methods: Association and Sequence Analysis
- 7 Validation and Application
-
3 All about data
-
Part III: Data mining in action
-
8 Marketing
- 8.1 Recipe 1: Response Optimisation: To Find and Address the Right Number of Customers
- 8.2 Recipe 2: To Find the x% of Customers with the Highest Affinity to an Offer
- 8.3 Recipe 3: To Find the Right Number of Customers to Ignore
- 8.4 Recipe 4: To Find the x% of Customers with the Lowest Affinity to an Offer
- 8.5 Recipe 5: To Find the x% of Customers with the Highest Affinity to Buy
- 8.6 Recipe 6: To Find the x% of Customers with the Lowest Affinity to Buy
- 8.7 Recipe 7: To Find the x% of Customers with the Highest Affinity to a Single Purchase
- 8.8 Recipe 8: To Find the x% of Customers with the Highest Affinity to Sign a Long-Term Contract in Communication Areas
- 8.9 Recipe 9: To Find the x% of Customers with the Highest Affinity to Sign a Long-Term Contract in Insurance Areas
-
9 Intra-Customer Analysis
- 9.1 Recipe 10: To Find the Optimal Amount of Single Communication to Activate One Customer
- 9.2 Recipe 11: To Find the Optimal Communication Mix to Activate One Customer
- 9.3 Recipe 12: To Find and Describe Homogeneous Groups of Products
- 9.4 Recipe 13: To Find and Describe Groups of Customers with Homogeneous Usage
- 9.5 Recipe 14: To Predict the Order Size of Single Products or Product Groups
- 9.6 Recipe 15: Product Set Combination
- 9.7 Recipe 16: To Predict the Future Customer Lifetime Value of a Customer
- 10 Learning from a Small Testing Sample and Prediction
- 11 Miscellaneous
- 12 Software and Tools
- 13 Overviews
-
8 Marketing
- Bibliography
- Index
- End User License Agreement
Product information
- Title: A Practical Guide to Data Mining for Business and Industry
- Author(s):
- Release date: May 2014
- Publisher(s): Wiley
- ISBN: 9781119977131
You might also like
book
Real-World Data Mining: Applied Business Analytics and Decision Making
Use the latest data mining best practices to enable timely, actionable, evidence-based decision making throughout your …
book
Data Mining for Business Analytics
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to …
book
Business Intelligence Strategy and Big Data Analytics
Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - …
video
Applied Data Mining for Business Analytics
Predictive Analytics, 2nd Edition is now available. Please use the new and expanded course. 6+ Hours …