This book, authored by Dr. David L. Olson and Dr. Dursun Delen, presents advanced data mining techniques that are effective in handling complex and uncertain data sets, which are challenging for traditional methods like logistic regression, neural networks, and decision trees. The book aims to introduce fundamental data mining concepts, demonstrate the potential of large data sets for business understanding, and cover various techniques and their applications in business settings. The book is divided into three parts. Part I introduces data mining concepts and the data mining process, including the CRISP-DM and SEMMA methodologies. Part II discusses various data mining methods, including memory-based reasoning, association rules, fuzzy sets, rough sets, support vector machines, and genetic algorithms. Part III focuses on business applications of these techniques, highlighting their value in decision-making. Each chapter includes real-world examples, data sets, and software references. The book provides a detailed overview of data mining processes, methods, and applications, with a focus on practical implementation and evaluation. It also covers the evaluation of predictive models, performance metrics, and the use of data mining in areas such as credit screening, product quality testing, customer targeting, and medical analysis. The book is intended for students and professionals seeking to understand and apply advanced data mining techniques in business contexts.This book, authored by Dr. David L. Olson and Dr. Dursun Delen, presents advanced data mining techniques that are effective in handling complex and uncertain data sets, which are challenging for traditional methods like logistic regression, neural networks, and decision trees. The book aims to introduce fundamental data mining concepts, demonstrate the potential of large data sets for business understanding, and cover various techniques and their applications in business settings. The book is divided into three parts. Part I introduces data mining concepts and the data mining process, including the CRISP-DM and SEMMA methodologies. Part II discusses various data mining methods, including memory-based reasoning, association rules, fuzzy sets, rough sets, support vector machines, and genetic algorithms. Part III focuses on business applications of these techniques, highlighting their value in decision-making. Each chapter includes real-world examples, data sets, and software references. The book provides a detailed overview of data mining processes, methods, and applications, with a focus on practical implementation and evaluation. It also covers the evaluation of predictive models, performance metrics, and the use of data mining in areas such as credit screening, product quality testing, customer targeting, and medical analysis. The book is intended for students and professionals seeking to understand and apply advanced data mining techniques in business contexts.