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EBOOK INFORMATIKA GRATIS

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Ebook Informatika Gratis

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Data Mining: Practical Machine Learning Tools and Techniques 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 mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time.

Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition.

In this new edition, all chapters have been revised and new topics brought in. A walk-through guide to existing open-source data mining software is also included in this edition.

Data Warehousing and Mining DWM is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead.

The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining DWM.

This encyclopedia consists of more than contributors from 32 countries, 1, terms and definitions, and more than 4, references. This book guides R users into data mining and helps data miners who use R in their work. It provides a how-to method using R for data mining applications from academia to industry.

For the many universities that have courses on data mining, this book is an invaluable reference for students studying data mining and its related subjects.

In addition, it is a useful resource for anyone involved in industrial training courses on data mining and analytics. The concepts in this book help readers as R becomes increasingly popular for data mining applications.

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