Data Analysis and Data Mining : An Introduction.
Title:
Data Analysis and Data Mining : An Introduction.
Author:
Azzalini, Adelchi.
ISBN:
9780199909285
Personal Author:
Physical Description:
1 online resource (308 pages)
Contents:
Cover Page -- Title Page -- Copyright Page -- Contents -- Preface -- Preface to The English Edition -- 1. Introduction -- 1.1. New Problems and New Opportunities -- 1.2. All Models are Wrong -- 1.3. A Matter of Style -- 2. A-B-C -- 2.1. Old Friends: Linear Models -- 2.2. Computational Aspects -- 2.3. Likelihood -- 2.4. Logistic Regression and GLM -- Exercises -- 3. Optimism, Conflicts, and Trade-Offs -- 3.1. Matching the Conceptual Frame and Real Life -- 3.2. A Simple Prototype Problem -- 3.3. If We Knew f(x) ... -- 3.4. But as We do not Know f(x) ... -- 3.5. Methods for Model Selection -- 3.6. Reduction of Dimensions and Selection of Most Appropriate Model -- Exercises -- 4. Prediction of Quantitative Variables -- 4.1. Nonparametric Estimation: Why? -- 4.2. Local Regression -- 4.3. The Curse of Dimensionality -- 4.4. Splines -- 4.5. Additive Models and GAM -- 4.6. Projection Pursuit -- 4.7. Inferential Aspects -- 4.8. Regression Trees -- 4.9. Neural Networks -- 4.10. Case Studies -- Exercises -- 5. Methods of Classification -- 5.1. Prediction of Categorical Variables -- 5.2. An Introduction Based on a Marketing Problem -- 5.3. Extension to Several Categories -- 5.4. Classification Via Linear Regression -- 5.5. Discriminant Analysis -- 5.6. Some Nonparametric Methods -- 5.7. Classification Trees -- 5.8. Some Other Topics -- 5.9. Combination of Classifiers -- 5.10. Case Studies -- Exercises -- 6. Methods of Internal Analysis -- 6.1. Cluster Analysis -- 6.2. Associations Among Variables -- 6.3. Case Study: Web Usage Mining -- Appendix A Complements of Mathematics and Statistics -- A.1. Concepts on Linear Algebra -- A.2. Concepts of Probability Theory -- A.3. Concepts of Linear Models -- Appendix B Data Sets -- B.1. Simulated Data -- B.2. Car Data -- B.3. Brazilian Bank Data -- B.4. Data for Telephone Company Customers -- B.5. Insurance Data.
B.6. Choice of Fruit Juice Data -- B.7. Customer Satisfaction -- B.8. Web Usage Data -- Appendix C Symbols and Acronyms -- References -- Author Index -- Subject Index.
Abstract:
An introduction to statistical data mining, Data Analysis and Data Mining is both textbook and professional resource. Assuming only a basic knowledge of statistical reasoning, it presents core concepts in data mining and exploratory statistical models to students and professional statisticians-both those working in communications and those working in a technological or scientific capacity-who have a limited knowledge of data mining.This book presents key statistical concepts by way of case studies, giving readers the benefit of learning from real problems and real data. Aided by a diverse range of statistical methods and techniques, readers will move from simple problems to complex problems. Through these case studies, authors Adelchi Azzalini and Bruno Scarpa explain exactly how statistical methods work; rather than relying on the "push the button" philosophy, they demonstrate how to use statistical tools to find the best solution to any given problem.Case studies feature current topics highly relevant to data mining, such web page traffic; the segmentation of customers; selection of customers for direct mail commercial campaigns; fraud detection; and measurements of customer satisfaction. Appropriate for both advanced undergraduate and graduate students, this much-needed book will fill a gap between higher level books, which emphasize technical explanations, and lower level books, which assume no prior knowledge and do not explain the methodology behind the statistical operations.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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