I like the comprehensive coverage which spans all major data mining techniques including classification, clustering, and pattern mining association rules. Pearson offers affordable and accessible purchase options to meet the needs of your students.
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The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning.
You have successfully signed out and will be required to sign back in should you need to download more resources. Introduction to Data Mining. Description Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time.
Quotes This book provides a comprehensive coverage of important data mining 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. Topics covered include classification, association analysis, clustering, anomaly detection, and avoiding false discoveries. Appendices: All appendices are available on the web.
A new appendix provides a brief discussion of scalability in the context of big data. No portion of this material may be reproduced, in any form or by any means, without permission in writing from the publisher.
Check out the preface for a complete list of features and what's new in this edition. 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. Please try again. The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. You have successfully signed out and will be required to sign back in should you need to download more resources.
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.
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