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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.
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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. 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.
Dr. 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.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 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 science or domain journals. Previous to his academic career, he held a variety of software engineering, analysis, and design positions in industry at Silicon Biology, Racotek, and NCR.
Dr. Anuj Karpatne is a Post Doctoral 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 lie in the development of data mining and machine learning algorithms for solving scientific and socially relevant problems in varied disciplines such as climate science, hydrology, and healthcare. His research has been published at top-tier journals and conferences such as SDM, ICDM, KDD, NIPS, TKDE, and ACM Computing Surveys.
Dr. Vipin Kumar is a Regents 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 care. Kumar's foundational research been 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), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards in high performance computing.
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Descrizione libro Pearson (edition 2). Hardcover. Condizione: New. 2. Brand new item, never opened! Ship within 24hrs. APO/FPO addresses supported. Codice articolo 0133128903-9-1
Descrizione libro Prentice Hall. Condizione: New. Brand New. Codice articolo 0133128903
Descrizione libro Pearson Education (US), United States, 2018. Paperback. Condizione: New. 2nd edition. Language: English. Brand new Book. Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course. It is also suitable for individuals seeking an introduction to data mining. The text assumes only a modest statistics or mathematics background, and no database knowledge is needed.Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students. It will help: Present Fundamental Concepts and Algorithms: Written for the beginner, this text provides both theoretical and practical coverage of all data mining topics.Support Learning: Instructor resources include solutions for exercises and a complete set of lecture slides. Codice articolo BTE9780133128901
Descrizione libro Condizione: New. Codice articolo 18733054-n
Descrizione libro Addison-Wesley, 2014. Hardcover. Condizione: New. Codice articolo DADAX0133128903
Descrizione libro PEL, 2013. Condizione: New. Codice articolo TV9780133128901
Descrizione libro Pearson, 2018. Hardcover. Condizione: New. NEW TEXTBOOK-SHIPS WITH EMAILED TRACKING. Codice articolo Sep18-9780133128901
Descrizione libro Pearson. Hardcover. Condizione: New. 0133128903 New Textbook, Ships with Emailed Tracking from USA. Codice articolo Z0133128903ZN
Descrizione libro Pearson. Hardcover. Condizione: New. 0133128903 New Condition. Codice articolo NEW99.4048430