Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 2nd Edition
About the Authors.
Chapter 1: Why and What Is Data Mining?
Chapter 2: The Virtuous Cycle of Data Mining.
Chapter 3: Data Mining Methodology and Best Practices.
Chapter 4: Data Mining Applications in Marketing and Customer Relationship Management.
Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools.
Chapter 6: Decision Trees.
Chapter 7: Artificial Neural Networks.
Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering.
Chapter 9: Market Basket Analysis and Association Rules.
Chapter 10: Link Analysis.
Chapter 11: Automatic Cluster Detection.
Chapter 12: Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing.
Chapter 13: Genetic Algorithms.
Chapter 14: Data Mining throughout the Customer Life Cycle.
Chapter 15: Data Warehousing, OLAP, and Data Mining.
Chapter 16: Building the Data Mining Environment.
Chapter 17: Preparing Data for Mining.
Chapter 18: Putting Data Mining to Work.
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Variance is calculated incorrectly:
(0.92 + 9*0.12)/10 = 0.09
(0.81 + 9*0.01)/10 = 0.09
Page 260, table 8.1. The headings $1,000 and $1,500 are reversed.
Page 262. The bit starting with the third sentence should read "For Shelter Island, the midpoint of the most common range is $875. . . . rent in Tuxedo of $1,062.50."
Page 299, table 9.2. Some of the co-occurrences are wrong. OJ+Soda should be 2. Soda+Detergent should be 1.
Page 310, table 9.6. There is an extraneous degree symbol in the heading.
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- Offers concise, clear, and practical explanations of complex concepts
- Covers core datat mining techinques, including: decision trees; neural networks; collaborative filtering; association rules, link analysis, clustering; and survival analysis
- Provides an overview of data mining best practices and how to perform data mining using simple tools like Excel.
- Includes advanced chapters which cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining at your company.