[EBOOK] Data Mining for Business Analytics Concepts, Techniques, and
Originally, "data mining" or "data dredging" was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errors one can make by trying to extract what really isn't in the data. Today, "data mining" has taken on a positive meaning.
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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: The Textbook. "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. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances.
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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, 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.
Saat ini selain mengajar matakuliah Data Mining di PTTIK Universitas Brawijaya, penulis juga mengadakan banyak penelitian dan menulis karya ilmiah di bidang data mining. Dian Eka Ratnawati, S.Si, M.Kom. lahir di Trenggalek, 19 Juni 1972. Beliau menyelesaikan studi S1 jurusan Matematika Institut Teknologi Sepuluh Nopember.
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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.
Data Mining Concepts and Techniques (3rd ed.) by Jiawei Han (ebook)
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: 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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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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Book description. Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration. Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.
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About This BookUnderstand the basics of data mining and why R is a perfect tool for it.Manipulate your data using popular R packages such as ggplot2, dplyr, and so on to gather valuable business insights from it.Apply effective data mining models to perform regression and classification tasks.Who This Book Is ForIf you are a budding data.
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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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Top Data Mining Books. 1. Introduction to Data Mining. by Tan, Steinbach & Kumar. Basically, this book is a very good introduction book for data mining. It discusses all the main topics of data mining that are clustering, classification, pattern mining, and outlier detection. Moreover, it contains two very good chapters on clustering by Tan.
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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.
Discovering Knowledge in Data An Introduction to Data Mining 2nd
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.