"数据挖掘导论：算法原理与技术英文第2版" - 美陈封能（2019）

Data Mining Introduction, Second Edition by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, and Vipin Kumar, provides a comprehensive introduction to the principles and techniques used in data mining from an algorithmic perspective. Understanding these principles and techniques is crucial in order to effectively apply data mining across various types of data. The book covers a range of topics including data preprocessing, predictive modeling, association analysis, clustering analysis, anomaly detection, and error avoidance. By introducing the basic concepts and algorithms of each topic, the authors equip readers with the necessary background and tools to apply data mining to real-world problems. Pang-Ning Tan, a professor in the Department of Computer Science and Engineering at Michigan State University, specializes in data mining, database systems, network cyber security, and network analysis. This collaboration between Tan and his co-authors, who are experts in their respective fields, ensures that readers receive a comprehensive and up-to-date understanding of data mining techniques and applications. Overall, Data Mining Introduction, Second Edition is a valuable resource for students, researchers, and practitioners looking to delve into the complex and rapidly evolving field of data mining. Whether you are looking to gain a theoretical understanding of data mining algorithms or apply them to practical problems, this book provides the necessary guidance and knowledge to help you succeed.