The "Data Mining and Knowledge Discovery Handbook" is a comprehensive guide to the field of data mining and knowledge discovery. The second edition, edited by Prof. Oded Maimon from Tel Aviv University and Dr. Lior Rokach from Ben-Gurion University of the Negev, covers the latest advancements and methodologies in the field. The handbook is structured into eight parts, each focusing on different aspects of data mining: 1. **Preprocessing Methods**: Topics include data cleansing, dimension reduction, and discretization. 2. **Supervised Methods**: Covers regression, decision trees, Bayesian networks, rule induction, and support vector machines. 3. **Unsupervised Methods**: Discusses clustering, association rules, link analysis, and visualization. 4. **Soft Computing Methods**: Reviews evolutionary algorithms, neural networks, and fuzzy logic. 5. **Supporting Methods**: Explores statistical methods, logics, wavelet methods, and fractal mining. 6. **Advanced Methods**: Includes multi-label data mining, privacy, meta-learning, and ensemble methods. 7. **Applications**: Focuses on data mining in various industries such as finance, medicine, and security. 8. **Software**: Provides an overview of commercial and open-source data mining software. The handbook aims to provide a detailed and up-to-date reference for researchers, scholars, students, and professionals, covering both classic and novel methods, as well as practical applications and software tools. It is designed to help readers understand and apply the latest techniques in data mining and knowledge discovery.The "Data Mining and Knowledge Discovery Handbook" is a comprehensive guide to the field of data mining and knowledge discovery. The second edition, edited by Prof. Oded Maimon from Tel Aviv University and Dr. Lior Rokach from Ben-Gurion University of the Negev, covers the latest advancements and methodologies in the field. The handbook is structured into eight parts, each focusing on different aspects of data mining: 1. **Preprocessing Methods**: Topics include data cleansing, dimension reduction, and discretization. 2. **Supervised Methods**: Covers regression, decision trees, Bayesian networks, rule induction, and support vector machines. 3. **Unsupervised Methods**: Discusses clustering, association rules, link analysis, and visualization. 4. **Soft Computing Methods**: Reviews evolutionary algorithms, neural networks, and fuzzy logic. 5. **Supporting Methods**: Explores statistical methods, logics, wavelet methods, and fractal mining. 6. **Advanced Methods**: Includes multi-label data mining, privacy, meta-learning, and ensemble methods. 7. **Applications**: Focuses on data mining in various industries such as finance, medicine, and security. 8. **Software**: Provides an overview of commercial and open-source data mining software. The handbook aims to provide a detailed and up-to-date reference for researchers, scholars, students, and professionals, covering both classic and novel methods, as well as practical applications and software tools. It is designed to help readers understand and apply the latest techniques in data mining and knowledge discovery.