The introductory chapter added the K-means initialization technique and an updated discussion of cluster evaluation. The advanced clustering chapter adds a new section on spectral graph clustering. Data: The data chapter has been updated to include discussions of mutual information and kernel-based techniques. Exploring Data: The data exploration chapter has been removed from the print edition of the book, but is available on the web.
Provides both theoretical and practical coverage of all data mining topics. Includes extensive number of integrated examples and figures. Offers instructor resources including solutions for exercises and complete set of lecture slides. Assumes only a modest statistics or mathematics background, and no database knowledge is needed.
Pearson offers affordable and accessible purchase options to meet the needs of your students. Connect with us to learn more. He received his M. 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.
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 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.
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. We're sorry! We don't recognize your username or password.
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Introduction to Data Mining, 2nd Edition. Description For courses in data mining and database systems. 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. Preface Preface is available for download in PDF format.
Reflects the changes in the industry New - As a result of developments in the industry, the text contains a deeper focus on big data and includes chapter changes in response to these advances. New - This edition contains new and updated approaches to data mining , specifically among the anomaly detection section. Updated - The classification chapters have been significantly changed to reflect the latest information in the industry, including a new section on deep learning and updates to the advanced classification chapter.
Encourages critical thinking and problem solving Updated - Discussion sections have been expanded, clarified, and now include new topics. Support materials , such as PowerPoint lecture slides, group projects, algorithms, and data sets are available online to promote continued learning and practice. Discuss whether or not each of the following activities is a data mining task. Aug 8, - Data mining does not replace other areas of data analysis, but rather Overview Specifically, this book provides a comprehensive introduction to Data Mining.
Vipin Kumar. University of Minnesota. Pang-Ning Tan. Michigan State University. Michael Steinbach. Skip to content Home.
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