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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.
Data Mining Practical Machine Learning Tools and Techniques
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 Techniques: For Marketing, Sales, and Customer Relationship Management. A data mining book oriented specifically to marketing and business management. With great case studies in order to understand how to apply these techniques on the real world. Inductive Logic Programming Techniques and Applications.
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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: 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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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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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.
Data Mining Concepts and Techniques (3rd ed.) by Jiawei Han (ebook)
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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Data Analytics: An Essential Beginner's Guide To Data Mining, Data Collection, Big Data Analytics For Business, And Business Intelligence ConceptsData Mining: The Data Mining Guide for Beginners, Including Applications for Business, Data Mining Techniques, Concepts, and MoreWith this book, not only will you understand all the internal nitty.
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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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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.
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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.
Data Mining and Predictive Analytics for Business Decisions
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.
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.