Data mining for dummies [Book] / Meta S. Brown.

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In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. It explains the details of the knowledge discovery process including: model creation, validity testing, and interpretation; effective communication of findings; available tools, both paid and open-source; and data selection, transformation, and evaluation. This guide takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. -- Edited summary from book

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. Details the advances that have taken place in fuzzy set theory and fuzzy logic in recent years. • Requires only a basic knowledge of classical (nonfuzzy) set theory, classical (two-valued) logic, and probability theory. • Includes all bibliographical, historical, and other side remarks in the notes that follow each individual chapter. • Includes a set of exercises after each chapter. • Offers an overview of neural networks, genetic algorithms, and rough sets in Appendices A-C. • Includes a glossary of key concepts and a glossary of symbols

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