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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, discrete sequences, spatial …
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"This book is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book's coverage of underlying concepts. A broad range of topics are covered, from an initial overview of ...
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Read this book and you will understand the Science behind thinking data."-- Ron Bekkerman Chief Data Officer at Carmel Ventures "A great book for business managers who lead or interact with data scientists, who wish to better understand the principles and algorithms available without the technical details of single-disciplinary …
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Data mining books frequently omit many basic machine learning methods such as linear, kernel, or logistic regression. However, machine learning books do not address basic data mining methods like association rules or outlier detection. This book finally provides about as complete coverage as one can hope to get from a single book.
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This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. Key features Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects
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This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including …
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Book #10: Data Mining and Data Warehousing: Principles and Practical Techniques. Author: Parteek Bhatia. Get the book here. Review: Very few books combine the concepts of data warehousing and mining in a well-formed way, unlike this volume. It's written in simple language and covers essential topics like decision trees, distance metrics, data ...
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This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with…
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There is a rich and varied set of libraries available in Python for data mining. This book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book …
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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 …
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We used this book in a class which was my first academic introduction to data mining. The book's strengths are that it does a good job covering the field as it was around the 2008-2009 timeframe. Included are discussions of exploring data, classification, clustering, association analysis, cluster analysis, and anomaly detection. ...
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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 …
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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 …
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Digging intelligently in different large databases, data mining aims to extract implicit, previously unknown and potentially useful information from data, since "knowledge is power". The goal of this book is to provide, in a friendly way, both theoretical concepts and, especially, practical techniques of this exciting field, ready to be ...
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The book presents a coherent and unified repository of data science and machine learning major concepts, theories, methods, ... His main areas of interest are Data Mining, Pattern Recognition, and Recommender Systems. Dr. Rokach is the author of over 70 refereed papers in leading journals, conference proceedings and book chapters. ...
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A book about bayesian networks that provide capabilities to solve very complex problems. Also discusses programming implementations on the Python language. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management A data mining book oriented specifically to marketing and business management.
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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 going, from …
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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 …
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About the Book ˜ is textbook explores the di˚ erent aspects of data mining from the fundamentals to the com-plex 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
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ISBN: 9780123814807. Read it now on the O'Reilly learning platform with a 10-day free trial. O'Reilly members get unlimited access to books, live events, courses curated by …
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Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a …
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Book description. 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 …
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Book description. Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.This highly anticipated third edition of the most acclaimed work on data …
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The book focuses on fundamental data mining concepts and techniques for discovering interesting patterns from data in various applications. Prominent techniques for …
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The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011 ... We're fighting to restore access to 500,000+ books in court this week. Join us! A line drawing of the Internet Archive headquarters building façade. An …
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Introduction to Data Mining introduces the fundamental concepts and algorithms of data mining. The text offers a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers and professionals. ... A Pearson eTextbook is an easy-to-use digital version …
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The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data. This Third Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. ... (SVD/PCA, wavelets, support vector machines).. Overall, it is an excellent book on classic and modern ...
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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, …
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