Introduction to data mining 2nd edition Pang-Ning Tan PDF
Presented in a clear and accessible way, the book presents fundamental concepts and algorithms for each topic, providing the reader with the necessary foundation for applying data mining to real problems. The text helps readers understand the nuances of the subject and includes important sections on classification, association analysis, and cluster analysis. This edition improves upon the first version of the book, published more than a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
About the authors
Dr. Pang-Ning Tan is a professor in the Department of Computer Science and Engineering at Michigan State University. He received his M.S. Degree in Physics and Ph.D. Graduated in Computer Science from the University of Minnesota. His research interests focus on the development of new data mining algorithms for a wide range of applications, including climate and ecological sciences, cybersecurity and network analysis.
Dr. Anuj Karpatne is a Postdoctoral Associate in the Department of Computer Science and Engineering at the University of Minnesota. He received his M.Tech in Mathematics and Computing from the Indian Institute of Technology Delhi, and a Ph.D. in Computer Science at the University of Minnesota under the guidance of Prof. Vipin Kumar. His research interests are in the development of data mining and machine learning algorithms to solve scientifically and socially relevant problems in diverse disciplines such as climate science, hydrology and health.
Dr. Vipin Kumar is Regent Professor at the University of Minnesota, where he holds the William Norris Endowed Chair in the Department of Computer Science and Engineering. His research interests include data mining, high-performance computing and their applications in climate/ecosystems and health. Kumar's fundamental research was honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of knowledge discovery and data mining (KDD).
Dr. Michael Steinbach is a Research Scientist in the Department of Computer Science and Engineering at the University of Minnesota, where he earned a B.S. degree in Mathematics, an M.S. degree in Statistics and M.S. and Ph.D. degrees in Computer Science. His research interests are in the areas of data mining, machine learning and statistical learning and their applications in fields such as climate, biology and medicine. This search resulted in over 100 articles published in the proceedings of leading data mining or computer science conferences or domain journals.