The book "Advanced Data Mining Techniques" by Dr. David L. Olson and Dr. Dursun Delen is designed to introduce and discuss recent data mining tools that are effective in handling complex and uncertain datasets, which often pose challenges for traditional methods like logistic regression, neural networks, and decision trees. The book is structured into three parts: Introduction, Data Mining Methods as Tools, and Applications. **Part I: Introduction** - **Chapter 1** provides an overview of data mining, its process, and its business applications. - **Chapter 2** delves into the detailed data mining process, including visualization techniques. **Part II: Data Mining Methods as Tools** - **Chapter 3** covers memory-based reasoning methods and their real-world applications. - **Chapter 4** discusses association rule methods, particularly market basket analysis. - **Chapter 5** explores fuzzy data mining approaches, including fuzzy decision trees and association rules. - **Chapter 6** introduces Rough Sets, a popular data mining method. - **Chapter 7** focuses on support vector machines and their advantages. - **Chapter 8** examines the use of genetic algorithms in data mining. - **Chapter 9** discusses methods for evaluating models in data mining. **Part III: Applications** - **Chapter 10** presents a range of successful applications of data mining techniques, highlighting their value in business decision-making. The book aims to provide a comprehensive understanding of data mining concepts and techniques, supported by practical examples and real-world applications.The book "Advanced Data Mining Techniques" by Dr. David L. Olson and Dr. Dursun Delen is designed to introduce and discuss recent data mining tools that are effective in handling complex and uncertain datasets, which often pose challenges for traditional methods like logistic regression, neural networks, and decision trees. The book is structured into three parts: Introduction, Data Mining Methods as Tools, and Applications. **Part I: Introduction** - **Chapter 1** provides an overview of data mining, its process, and its business applications. - **Chapter 2** delves into the detailed data mining process, including visualization techniques. **Part II: Data Mining Methods as Tools** - **Chapter 3** covers memory-based reasoning methods and their real-world applications. - **Chapter 4** discusses association rule methods, particularly market basket analysis. - **Chapter 5** explores fuzzy data mining approaches, including fuzzy decision trees and association rules. - **Chapter 6** introduces Rough Sets, a popular data mining method. - **Chapter 7** focuses on support vector machines and their advantages. - **Chapter 8** examines the use of genetic algorithms in data mining. - **Chapter 9** discusses methods for evaluating models in data mining. **Part III: Applications** - **Chapter 10** presents a range of successful applications of data mining techniques, highlighting their value in business decision-making. The book aims to provide a comprehensive understanding of data mining concepts and techniques, supported by practical examples and real-world applications.