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Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD.
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The text helps you understand the nuances of the subject, and includes important sections on classification, association analysis and cluster analysis. This 2nd Editionimproves 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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Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process.
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Purpose. The purpose of this study is to examine the evidence-based use patterns of Higher Education Commission (HEC) subscribed e-books databases by the academic community at institutions of higher education in Pakistan. The study also investigates the differences in usage based on points of access, scholarly disciplines and gender of users.
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Data Mining: Concepts and Techniques, Edition 4 - Ebook written by Jiawei Han, Jian Pei, Hanghang Tong. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Data Mining: Concepts and Techniques, Edition 4.
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Other titles: Data mining and analysis Description: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2020. j Revised edition of: Data mining and analysis. 2014. j Includes bibliographical references and index. Identiers: LCCN 2019037293 (print) j LCCN 2019037294 (ebook) j ISBN 9781108473989 (hardback) j ISBN 9781108564175.
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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 organised into two chapters, beginning with basic concepts that provide necessary background for understanding each.
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Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility.
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Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!) "This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built.
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This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with.
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get.
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Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get.
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Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining. Key features. Readership. Table of contents. Product details. Purchase Data Mining - 4th Edition. Print Book & E-Book. ISBN 9780128117606, 9780128117613.
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Data Mining. : Charu C. Aggarwal. Springer, Apr 13, 2015 - Computers - 734 pages. This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems.
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About this book. This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series.