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

By: Contributor(s): Material type: TextTextSeries: --For dummiesDescription: 1 online resourceISBN:
  • 9781118893173
  • 1118893190
  • 9781118893166
  • 1118893166
Subject(s): DDC classification:
  • 006.312 23
Other classification:
  • 006.312
Summary: 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 bookSummary: . 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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Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books COMSATS University Wah Campus Main 006.312 BRO 23220 (Browse shelf(Opens below)) Available 10004000023220
Books Books COMSATS University Wah Campus 511.3219 KLI (Browse shelf(Opens below)) Available 10004000003759
Books Books COMSATS University Wah Campus 511.3219 KLI (Browse shelf(Opens below)) Available 10004000006028
Books Books COMSATS University Wah Campus Main 511.3219 KLI 23224 (Browse shelf(Opens below)) Available 10004000023224
Books Books COMSATS University Wah Campus Main 005.43 STA 23073 (Browse shelf(Opens below)) Available 10004000023073
Books Books COMSATS University Wah Campus Main 005.43 STA 23293 (Browse shelf(Opens below)) Available 10004000023293
Books Books COMSATS University Wah Campus Main 005.43 STA 23686 (Browse shelf(Opens below)) Available 10004000023686
Books Books COMSATS University Wah Campus Main 005.43 STA (Browse shelf(Opens below)) Available 10004000024014
Books Books COMSATS University Wah Campus Main 005.43 STA (Browse shelf(Opens below)) Available 10004000024015
Books Books COMSATS University Wah Campus Main 005.43 STA (Browse shelf(Opens below)) Available 10004000024016
Books Books COMSATS University Wah Campus Main 005.43 STA (Browse shelf(Opens below)) Available 10004000024017
Books Books COMSATS University Wah Campus Main 005.43 STA 24018 (Browse shelf(Opens below)) 24018 Available 10004000024018
Total holds: 0

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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

All.

. 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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All.

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