Book description
The leading introductory book on data mining, fully updated and revised!
When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition—more than 50% new and revised—is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company.
Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems
Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately
Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more
Provides best practices for performing data mining using simple tools such as Excel
Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Table of contents
- Cover
- Title Page
- Copyright
- Dedication
- About the Authors
- Credits
- Acknowledgments
- Introduction
- Chapter 1: What Is Data Mining and Why Do It?
-
Chapter 2: Data Mining Applications in Marketing and Customer Relationship Management
- Two Customer Lifecycles
- Organize Business Processes Around the Customer Lifecycle
- Data Mining Applications for Customer Acquisition
- A Data Mining Example: Choosing the Right Place to Advertise
- Data Mining to Improve Direct Marketing Campaigns
- Using Current Customers to Learn About Prospects
- Data Mining Applications for Customer Relationship Management
- Retention
- Beyond the Customer Lifecycle
- Lessons Learned
- Chapter 3: The Data Mining Process
- Chapter 4: What You Should Know About Data
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Chapter 5: Descriptions and Prediction: Profiling and Predictive Modeling
- Directed Data Mining Models
- Directed Data Mining Methodology
- Step 1: Translate the Business Problem into a Data Mining Problem
- Step 2: Select Appropriate Data
- Step 3: Get to Know the Data
- Step 4: Create a Model Set
- Step 5: Fix Problems with the Data
- Step 6: Transform Data to Bring Information to the Surface
- Step 7: Build Models
- Step 8: Assess Models
- Step 9: Deploy Models
- Step 10: Assess Results
- Step 11: Begin Again
- Lessons Learned
- Chapter 6: Data Mining Using Classic Statistical Techniques
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Chapter 7: Decision Trees
- What Is a Decision Tree and How Is It Used?
- Decision Trees Are Local Models
- Growing Decision Trees
- Finding the Best Split
- Pruning
- Extracting Rules from Trees
- Decision Tree Variations
- Assessing the Quality of a Decision Tree
- When Are Decision Trees Appropriate?
- Case Study: Process Control in a Coffee Roasting Plant
- Lessons Learned
-
Chapter 8: Artificial Neural Networks
- A Bit of History
- The Biological Model
- Artificial Neural Networks
- A Sample Application: Real Estate Appraisal
- Training Neural Networks
- Radial Basis Function Networks
- Neural Networks in Practice
- Choosing the Training Set
- Preparing the Data
- Interpreting the Output from a Neural Network
- Neural Networks for Time Series
- Can Neural Network Models Be Explained?
- Lessons Learned
-
Chapter 9: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
- Memory-Based Reasoning
- Challenges of MBR
- Case Study: Using MBR for Classifying Anomalies in Mammograms
- Measuring Distance and Similarity
- The Combination Function: Asking the Neighbors for Advice
- Case Study: Shazam — Finding Nearest Neighbors for Audio Files
- Collaborative Filtering: A Nearest-Neighbor Approach to Making Recommendations
- Lessons Learned
- Chapter 10: Knowing When to Worry: Using Survival Analysis to Understand Customers
- Chapter 11: Genetic Algorithms and Swarm Intelligence
- Chapter 12: Tell Me Something New: Pattern Discovery and Data Mining
- Chapter 13: Finding Islands of Similarity: Automatic Cluster Detection
- Chapter 14: Alternative Approaches to Cluster Detection
- Chapter 15: Market Basket Analysis and Association Rules
- Chapter 16: Link Analysis
- Chapter 17: Data Warehousing, OLAP, Analytic Sandboxes, and Data Mining
- Chapter 18: Building Customer Signatures
- Chapter 19: Derived Variables: Making the Data Mean More
- Chapter 20: Too Much of a Good Thing? Techniques for Reducing the Number of Variables
- Chapter 21: Listen Carefully to What Your Customers Say: Text Mining
- Index
Product information
- Title: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition
- Author(s):
- Release date: March 2011
- Publisher(s): Wiley
- ISBN: 9780470650936
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