Data Analysis and Data Mining: An Introduction

相似文件 换一批

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

by:Adelchi Azzalini, Bruno Scarpa

    An introduction to statistical data mining, Data Analysis and Data Mining: An Introduction 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.

Table of Contents
1. Introduction
2. A–B–C
3. Optimism, Conflicts, and Trade-offs
4. Prediction of Quantitative Variables
5. Methods of Classification
6. Methods of Internal Analysis
Appendix A: Complements of Mathematics and Statistics
Appendix B: Data Sets
Appendix C: Symbols and Acronyms

Book Details
Title: Data Analysis and Data Mining: An Introduction
Author: Adelchi Azzalini, Bruno Scarpa
Length: 288 pages
Edition: 1
Language: English
Publisher: Oxford University Press, USA
Publication Date: 2012-04-23
ISBN-10: 0199767106
ISBN-13: 9780199767106

Data.Analysis.and.Data.Mining.An.Introduction.pdf (3.53 MB, 需要: 1 个论坛币)

2014-1-6 09:35:05 上传

流量币
需要: 1 个论坛币  [购买]

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏2 回帖

关键词：introduction Data Mining troduction Analysis Analysi scientific knowledge concepts limited

Table of Contents
Preface vii
PrefacetotheEnglishEdition ix
1. Introduction 1
1.1. Newproblemsandnewopportunities 1
1.2. Allmodelsarewrong 9
1.3. Amatterofstyle 12
2. A–B–C 15
2.1. Oldfriends:Linearmodels 15
2.2. Computationalaspects 30
2.3. Likelihood 33
2.4. LogisticregressionandGLM 40
Exercises 44
3. Optimism,Conflicts,andTrade-offs 45
3.1. Matchingtheconceptualframeandreallife 45
3.2. Asimpleprototypeproblem 46
3.3. Ifweknew f(x) ... 47
3.4. Butaswedonotknow f(x) ... 51
3.5. Methodsformodelselection 52
3.6. Reductionofdimensionsandselectionofmost
appropriatemodel 58
Exercises 66
4. PredictionofQuantitativeVariables 68
4.1. Nonparametricestimation:Why? 68
4.2. Localregression 69
4.3. Thecurseofdimensionality 78
4.4. Splines 79
4.5. AdditivemodelsandGAM 89
4.6. Projectionpursuit 93
4.7. Inferentialaspects 94
4.8. Regressiontrees 98
4.9. Neuralnetworks 106
4.10.Casestudies 111
Exercises 132
5. MethodsofClassification 134
5.1. Predictionofcategoricalvariables 134
5.2. Anintroductionbasedonamarketingproblem 135
5.3. Extensiontoseveralcategories 142
5.4. Classificationvialinearregression 149
5.5. Discriminantanalysis 154
5.6. Somenonparametricmethods 159
5.7. Classificationtrees 164
5.8. Someothertopics 168
5.9. Combinationofclassifiers 176
5.10.Casestudies 183
Exercises 210
6. MethodsofInternalAnalysis 212
6.1. Clusteranalysis 212
6.2. Associationsamongvariables 222
6.3. Casestudy:Webusagemining 232
AppendixA ComplementsofMathematicsandStatistics 240
A.1. Conceptsonlinearalgebra 240
A.2. Conceptsofprobabilitytheory 241
A.3. Conceptsoflinearmodels 246
AppendixB DataSets 254
B.1. Simulateddata 254
B.2. Cardata 254
B.3. Brazilianbankdata 255
B.4. Datafortelephonecompanycustomers 256
B.5. Insurancedata 257
B.6. Choiceoffruitjuicedata 258
B.7. Customersatisfaction 259
B.8. Webusagedata 261
AppendixC SymbolsandAcronyms 263
References 265
AuthorIndex 269
SubjectIndex 271

好，赞一个！

thanks.