Data Mining Techniques – Michael Berry, Gordon Linoff – 2nd Edition


Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems.

Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support. The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining.

More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining. Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis.

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  • Chapter 1 What Is Data Mining and Why Do It?
    Chapter 2 Data Mining Applications in Marketing and Customer Relationship Management.
    Chapter 3 The Data Mining Process.
    Chapter 4 Statistics 101: What You Should Know About Data.
    Chapter 5 Descriptions and Prediction: Profiling and Predictive Modeling.
    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 Reasoning and Collaborative Filtering.
    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.
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