Data Mining Techniques: For Marketing, Sales, and Customer Relationship ManagementThe 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.
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. |
Contents
1 | |
6 | |
7 | |
13 | |
15 | |
27 | |
67 | |
What You Should Know About Data | 101 |
Lessons Learned | 457 |
Automatic Cluster Detection | 459 |
Alternative Approaches to Cluster Detection | 499 |
Gaussian Mixture Models | 505 |
Divisive Clustering | 513 |
SelfOrganizing Maps | 527 |
Market Basket Analysis and Association Rules | 535 |
Association Analysis | 547 |
Multiple Comparisons | 129 |
Profiling | 151 |
Transform Data to Bring Information | 180 |
Deploy Models | 190 |
Data Mining Using Classic Statistical Techniques | 195 |
Similarity Models | 196 |
Table Lookup Models | 203 |
Naïve Bayesian Models | 210 |
Multiple Regression | 220 |
Decision Trees | 237 |
Finding the Best Split | 252 |
Lessons Learned | 279 |
Artificial Neural Networks | 281 |
Lessons Learned | 319 |
MemoryBased | 321 |
Measuring Distance and Similarity | 335 |
Lessons Learned | 354 |
Using Survival Analysis | 357 |
Genetic Algorithms and Swarm Intelligence | 397 |
Pattern Discovery | 429 |
Extending the Ideas | 569 |
Link Analysis | 581 |
Lessons Learned | 612 |
Data Warehousing OLAP Analytic Sandboxes and Data Mining | 613 |
Where Does OLAP Fit In? | 639 |
Building Customer Signatures | 655 |
Making the Data Mean More | 693 |
Combining Variables | 707 |
Lessons Learned | 733 |
Too Much of a Good Thing? Techniques for Reducing the Number of Variables | 735 |
Principal Components | 753 |
Variable Clustering | 768 |
Lessons Learned | 774 |
Text Mining | 775 |
Ad Hoc Text Mining | 786 |
From Text to Numbers | 794 |
Sentiment Analysis | 806 |
Lessons Learned | 819 |
Index | 821 |