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WISDOM: Weighted Incremental Spatio-Temporal Multi-Task Learning via Tensor Decomposition

 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.

Introduction to Data Mining (2nd Edition) (What's New in Computer Science)

Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He received his MS degree in Physics and PhD degree in Computer Science from University of Minnesota. His research interests are in data mining and machine learning with application to various scientific and engineering domains, particularly in environmental and ecological sciences, network science, and cybersecurity. He has published more than 150 peer-reviewed articles and co-authored a widely-used textbook on data mining. His research has been supported by NSF, NASA, ONR, NOAA, NIH, and ARO, and through gifts from Cisco, Bosch Research, Hewlett Packard Research, and Narus.

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