Linoff / Berry Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management
3. Auflage 2011
ISBN: 978-1-118-08750-3
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 896 Seiten, E-Book

The leading introductory book on data mining, fully updated andrevised!
When Berry and Linoff wrote the first edition of Data MiningTechniques in the late 1990s, data mining was just starting tomove out of the lab and into the office and has since grown tobecome an indispensable tool of modern business. This newedition--more than 50% new and revised-- is asignificant update from the previous one, and shows you how toharness the newest data mining methods and techniques to solvecommon business problems. The duo of unparalleled authors shareinvaluable advice for improving response rates to direct marketingcampaigns, identifying new customer segments, and estimating creditrisk. In addition, they cover more advanced topics such aspreparing data for analysis and creating the necessaryinfrastructure for data mining at your company.
* Features significant updates since the previous edition andupdates you on best practices for using data mining methods andtechniques for solving common business problems
* Covers a new data mining technique in every chapter along withclear, concise explanations on how to apply each techniqueimmediately
* Touches on core data mining techniques, including decisiontrees, neural networks, collaborative filtering, association rules,link analysis, survival analysis, and more
* Provides best practices for performing data mining using simpletools such as Excel
Data Mining Techniques, Third Edition covers a new datamining technique with each successive chapter and then demonstrateshow you can apply that technique for improved marketing, sales, andcustomer support to get immediate results.

Autoren/Hrsg.

Linoff, Gordon S.

Berry, Michael J.

Inhaltsverzeichnis

Introduction.
Chapter 1 What Is Data Mining and Why Do It?
Chapter 2 Data Mining Applications in Marketing and CustomerRelationship Management.
Chapter 3 The Data Mining Process.
Chapter 4 Statistics 101: What You Should Know About Data.
Chapter 5 Descriptions and Prediction: Profiling and PredictiveModeling.
Chapter 6 Data Mining Using Classic Statistical Techniques.
Chapter 7 Decision Trees.
Chapter 8 Artifi cial Neural Networks.
Chapter 9 Nearest Neighbor Approaches: Memory-Based Reasoningand Collaborative Filtering.
Chapter 10 Knowing When to Worry: Using Survival Analysis toUnderstand Customers.
Chapter 11 Genetic Algorithms and Swarm Intelligence.
Chapter 12 Tell Me Something New: Pattern Discovery and DataMining.
Chapter 13 Finding Islands of Similarity: Automatic ClusterDetection.
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 DataMining.
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 theNumber of Variables.
Chapter 21 Listen Carefully to What Your Customers Say: TextMining.
Index.

Über Autor(innen)

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.