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基本説明
A comprehensive, professional reference book for scientists, engineers and researchers. Features: Provides key statistical analysis methods; Descriptions of new algorithms for Al/Machine Learning; Extensive case studies, examples, tutorials, MS Powerpoint slides, and datasets; Full glossary of data mining terminology.
Full Description
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.
Contents
PrefaceForwardsIntroductionPART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining ProcessChapter 1. History - The Phases of Data Analysis throughout the Ages Chapter 2. TheoryChapter 3. The Data Mining ProcessChapter 4. Data Understanding and PreparationChapter 5. Feature Selection - Selecting the Best Variables Chapter 6: Accessory Tools and Advanced Features in Data PART II: - The Algorithms in Data Mining and Text Mining, and the Organization of the Three most common Data Mining ToolsChapter 7. Basic AlgorithmsChapter 8: Advanced Algorithms Chapter 9. Text Mining Chapter 10. Organization of 3 Leading Data Mining Tools Chapter 11. Classification Trees = Decision Trees Chapter 12. Numerical Prediction (Neural Nets and GLMChapter 13. Model Evaluation and Enhancement Chapter 14. Medical Informatics Chapter 15. BioinformaticsChapter 16. Customer Response Models Chapter 17. Fraud Detection PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to learn how to do Data Mining AnalysesTutorials PART IV: Paradox of Complex Models; using the "right model for the right use", on-going development, and the Future.Chapter 18: Paradox of Ensembles and Complexity Chapter 19: The Right Model for the Right Use Chapter 20: The Top 10 Data Mining Mistakes Chapter 21: Prospect for the Future - Developing Areas in Data MiningChapter 22: SummaryGLOSSARY of STATISICAL and DATA MINING TERMS INDEXCD - With Additional Tutorials, data sets, Power Points, and Data Mining software
