Data analysis and data mining : an introduction / Adelchi Azzalini and Bruno Scarpa ; [text revised by Gabriel Walton].

Format:

Book

Author/Creator:

Azzalini, Adelchi.

Contributor:

Scarpa, Bruno.

Walton, Gabriel.

Standardized Title:

Analisi dei dati e "data mining". English

Language:

English

Subjects (All):

Data mining.

Physical Description:

1 online resource (289 p.)

Edition:

1st ed.

Place of Publication:

Oxford ; New York : Oxford University Press, c2012.

Language Note:

English

Summary:

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 rang

Contents:

Cover; 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

Exercises4. 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 trees5.8. 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 satisfactionB.8. Web usage data; Appendix C: Symbols and Acronyms; References; Author Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; V; W; Z; Subject Index; A; B; C; D; E; F; G; H; I; K; L; M; N; O; P; Q; R; S; T; U; V; W

Notes:

Description based upon print version of record.

Includes bibliographical references and indexes.

Description based on metadata supplied by the publisher and other sources.

ISBN:

0-19-994271-4

1-280-59575-2

9786613625588

0-19-990928-8

OCLC:

793996687