Introducing the fundamental concepts and algorithms of data mining Introduction to Data Mining, 2nd Edition , gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals.
Presented in a clear and accessible way, the book outlines fundamental concepts and algorithms for each topic, thus providing the reader with the necessary background for the application of 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 on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.
About the Author: Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University.
He received his M.
degree in Physics and Ph.
degree in Computer Science from University of Minnesota.
His research interests focus on the development of novel data mining algorithms for a broad range of applications, including climate and ecological sciences, cybersecurity, and network analysis.
He has published more than 130 technical papers in the area of data mining, including top conferences and journals such as KDD, ICDM, SDM, CIKM, and TKDE.
Michael Steinbach is a Research Scientist in the department of Computer Science and Engineering at the University of Minnesota, from which he earned a B.
degree in Mathematics, an M.
degree in Statistics, and M.
and Ph.
degrees in Computer Science.
His research interests are in the areas of data mining, machine learning, and statistical learning and its applications to fields, such as climate, biology, and medicine.
This research has resulted in more than 100 papers published in the proceedings of major data mining conferences or computer scien.
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Pearson Introduction to data mining, hardcover/pang-ning tan
