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Data Mining Techniques

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The leading introductory book on data mining, fully updated and revised!When Berry and Linoff wrote the first edition of Data Mini... Weiterlesen
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Beschreibung

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.

GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management.



Autorentext

GORDON S. LINOFF and MICHAEL J. A. BERRY are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored two of the leading data mining titles in the field, Data Mining Techniques and Mastering Data Mining (both from Wiley). They each have decades of experience applying data mining techniques to business problems in marketing and customer relationship management.



Klappentext

The newest edition of the leading introductory book on data mining, fully updated and revised

Who will remain a loyal customer and who won't? Which messages are most effective with which segments? How can customer value be maximized? This book supplies powerful tools for extracting the answers to these and other crucial business questions from the corporate databases where they lie buried. In the years since the first edition of this book, data mining has grown to become an indispensable tool of modern business. In this latest edition, Linoff and Berry have made extensive updates and revisions to every chapter and added several new ones. The book retains the focus of earlier editionsshowing marketing analysts, business managers, and data mining specialists how to harness data mining methods and techniques to solve important business problems. While never sacrificing accuracy for the sake of simplicity, Linoff and Berry present even complex topics in clear, concise English with minimal use of technical jargon or mathematical formulas. Technical topics are illustrated with case studies and practical real-world examples drawn from the authors' experiences, and every chapter contains valuable tips for practitioners. Among the techniques newly covered, or covered in greater depth, are linear and logistic regression models, incremental response (uplift) modeling, naïve Bayesian models, table lookup models, similarity models, radial basis function networks, expectation maximization (EM) clustering, and swarm intelligence. New chapters are devoted to data preparation, derived variables, principal components and other variable reduction techniques, and text mining.

After establishing the business context with an overview of data mining applications, and introducing aspects of data mining methodology common to all data mining projects, the book covers each important data mining technique in detail.

This third edition of Data Mining Techniques covers such topics as:

  • How to create stable, long-lasting predictive models

  • Data preparation and variable selection

  • Modeling specific targets with directed techniques such as regression, decision trees, neural networks, and memory based reasoning

  • Finding patterns with undirected techniques such as clustering, association rules, and link analysis

  • Modeling business time-to-event problems such as time to next purchase and expected remaining lifetime

  • Mining unstructured text

The companion website provides data that can be used to test out the various data mining techniques in the book.



Zusammenfassung
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 editionmore 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.



Inhalt

Introduction xxxvii

Chapter 1 What Is Data Mining and Why Do It? 1

What Is Data Mining? 2

Data Mining Is a Business Process 2

Large Amounts of Data 3

Meaningful Patterns and Rules 3

Data Mining and Customer Relationship Management 4

Why Now? 6

Data Is Being Produced 6

Data Is Being Warehoused 6

Computing Power Is Affordable 7

Interest in Customer Relationship Management Is Strong 7

Commercial Data Mining Software Products Have Become Available 8

Skills for the Data Miner 9

The Virtuous Cycle of Data Mining 9

A Case Study in Business Data Mining 11

Identifying BofA's Business Challenge 12

Applying Data Mining 12

Acting on the Results 13

Measuring the Effects of Data Mining 14

Steps of the Virtuous Cycle 15

Identify Business Opportunities 16

Transform Data into Information 17

Act on the Information 19

Measure the Results 20

Data Mining in the Context of the Virtuous Cycle 23

Lessons Learned 26

Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management 27

Two Customer Lifecycles 27

The Customer's Lifecycle 28

The Customer Lifecycle 28

Subscription Relationships versus Event-Based Relationships 30

Organize Business Processes Around the Customer Lifecycle 32

Customer Acquisition 33

Customer Activation 36

Customer Relationship Management 37

Winback 38

Data Mining Applications for Customer Acquisition 38

Identifying Good Prospects 39

Choosing a Communication Channel 39

Picking Appropriate Messages 40

A Data Mining Example: Choosing the Right Place to Advertise 40

Who Fits the Profile? 41

Measuring Fitness for Groups of Readers 44

Data Mining to Improve Direct Marketing Campaigns 45

Response Modeling 46

Optimizing Response for a Fixed Budget 47

Optimizing Campaign Profitability 49

Reaching the People Most Influenced by the Message 53

Using Current Customers to Learn About Prospects 54

Start Tracking Customers Before They Become Customers 55

Gather Information from New Customers 55

Acquisition-Time Variables Can Predict Future Outcomes 56

Data Mining Applications for Customer Relationship Management 56

Matching Campaigns to Customers 56

Reducing Exposure to Credit Risk 58

Determining Customer Value 59

Cross-selling, Up-selling, and Making Recommendations 60

Retention 60

Recognizing Attrition 60

Why Attrition Matters 61

Different Kinds of Attrition 62

Different Kinds of Attrition Model 63

Beyond the Customer Lifecycle 64

Lessons Learned 65

Chapter 3 The Data Mining Process 67

What Can Go Wrong? 68

Learning Things That Aren't True 68

Learning Things That Are True, but Not Useful 73

Data Mining Styles 74

Hypothesis Testing 75

Directed Data Mining 81

Undirected Data Mining 81

Goals, Tasks, and Techniques 82

Data Mining Business Goals 82

Data Mining Tasks 83

Data Mining Techniques 88

Formulating Data Mining Problems: From Goals to Tasks to Techniques 88

What Techniques for Which Tasks? 95

Is There a Target or Targets? 96

What Is the Target Data Like? 96

What Is the Input Data Like? 96

How Important Is Ease of Use? 97

How Important Is Model Explicability? 97

Lessons Learned 98

Chapter 4 Statistics 101: What You Should Know About Data 101

Occam's Razor 103

Skepticism and Simpson's Paradox 103

The Null Hypothesis 104

P-Values 105

Looking At and Measuring Data 106

Categorical Values 106

Numeric Variables 117

A Couple More Statistical Ideas 120

Measuring Response 120

Standard Error of a Proportion 121

Comparing Results Using Confidence Bounds 123

Comparing Results Using Difference of Proportions 124

Size of Sample 125

What the Confidence Interval Really Means 126

Size of Test and Control for an Experiment 127

Multiple Comparisons 129

The Confidence Level with Multiple Comparisons 129

Bonferroni's Correction 129

Chi-Square Test 130

Expected Values 130

Chi-Square Value 132

Comparison of Chi-Square to Difference of Proportions 134

An Example: Chi-Square for Regions and Starts 134

Case Study: Comparing Two Recommendation Systems with an A/B Test 138

First Metric: Participating Sessions 140

Data Mining and Statistics 144

Lessons Learned 148

Chapter 5 Descriptions and Prediction: Profiling and Predictive Modeling 151

Directed Data Mining Models 152

Defining the Model Structu...

Produktinformationen

Titel: Data Mining Techniques
Untertitel: For Marketing, Sales, and Customer Relationship Management
Autor:
EAN: 9781118087459
ISBN: 978-1-118-08745-9
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (epub)
Herausgeber: Wiley
Genre: Informatik
Anzahl Seiten: 888
Veröffentlichung: 23.03.2011
Jahr: 2011
Auflage: 3. Aufl.
Untertitel: Englisch
Dateigrösse: 24.5 MB