The second edition of the *Data Mining and Knowledge Discovery Handbook* is a comprehensive guide to the field of data mining and knowledge discovery. It covers a wide range of topics, including preprocessing methods, supervised and unsupervised learning, soft computing methods, supporting methods, advanced methods, and applications in various industries. The handbook is organized into eight parts, each containing several chapters that describe different methods, theories, and applications in data mining. The first part discusses preprocessing methods such as data cleansing, handling missing values, feature extraction, and discretization. The second part covers supervised methods like regression, decision trees, Bayesian networks, and support vector machines. The third part discusses unsupervised methods such as clustering, association rules, and link analysis. The fourth part explores soft computing methods, including fuzzy logic, neural networks, and evolutionary algorithms. Parts five and six present supporting and advanced methods in data mining, such as statistical methods, logics, query languages, text mining, web mining, causal discovery, and ensemble methods. Part seven provides an in-depth description of data mining applications in various industries, including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. The final part surveys software and tools available for data mining. The handbook is a valuable resource for researchers, scholars, students, and professionals in the field of data mining. It provides a comprehensive and concise reference to data mining and includes additional selected references for further study. The second edition includes about half new content, reflecting the latest advances in the field, including new methods, new applications, and new data types. The handbook is based on the authors' experiences in the field and aims to provide a coherent and unified repository of major concepts, theories, methodologies, trends, challenges, and applications in data mining.The second edition of the *Data Mining and Knowledge Discovery Handbook* is a comprehensive guide to the field of data mining and knowledge discovery. It covers a wide range of topics, including preprocessing methods, supervised and unsupervised learning, soft computing methods, supporting methods, advanced methods, and applications in various industries. The handbook is organized into eight parts, each containing several chapters that describe different methods, theories, and applications in data mining. The first part discusses preprocessing methods such as data cleansing, handling missing values, feature extraction, and discretization. The second part covers supervised methods like regression, decision trees, Bayesian networks, and support vector machines. The third part discusses unsupervised methods such as clustering, association rules, and link analysis. The fourth part explores soft computing methods, including fuzzy logic, neural networks, and evolutionary algorithms. Parts five and six present supporting and advanced methods in data mining, such as statistical methods, logics, query languages, text mining, web mining, causal discovery, and ensemble methods. Part seven provides an in-depth description of data mining applications in various industries, including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. The final part surveys software and tools available for data mining. The handbook is a valuable resource for researchers, scholars, students, and professionals in the field of data mining. It provides a comprehensive and concise reference to data mining and includes additional selected references for further study. The second edition includes about half new content, reflecting the latest advances in the field, including new methods, new applications, and new data types. The handbook is based on the authors' experiences in the field and aims to provide a coherent and unified repository of major concepts, theories, methodologies, trends, challenges, and applications in data mining.