Handbook of Statistical Analysis and Data Mining Applications

✍ Scribed by Robert Nisbet; John F Elder; Gary Miner

Publisher: Academic Press/Elsevier
Year: 2009
Tongue: English
Leaves: 860
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.

Written ''By Practitioners for Practitioners''

Non-technical explanations build understanding without jargon and equations

Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software

Practical advice from successful real-world implementations

Includes extensive case studies, examples, MS PowerPoint slides and datasets

CD-DVD with valuable fully-working 90-day software included: ''Complete Data Miner - QC-Miner - Text Miner'' bound with book

✦ Table of Contents

Content: History of phases of data analysis, basic theory, and the data mining process --
The algorithms in data mining and text mining, the organization of the three most common data mining tools, and selected specialized areas using data mining --
Tutorials--step-by-step case studies as a starting point to learn how to do data mining analyses --
Measuring true complexity, the ''right model for the right use,'' top mistakes, and the future of analytics.

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