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Discovering Knowledge in Data

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The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, pred... Weiterlesen
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Beschreibung

  • The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.
  • Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization
  • Offers extensive coverage of the R statistical programming language
  • Contains 280 end-of-chapter exercises
  • Includes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book


Autorentext

Daniel T. Larose earned his PhD in Statistics at the University of Connecticut. He is Professor of Mathematical Sciences and Director of the Data Mining programs at Central Connecticut State University. His consulting clients have included Microsoft, Forbes Magazine, the CIT Group, KPMG International, Computer Associates, and Deloitte, Inc. This is Larose's fourth book for Wiley.

Chantal D. Larose is an Assistant Professor of Statistics & Data Science at Eastern Connecticut State University (ECSU). She has co-authored three books on data science and predictive analytics. She helped develop data science programs at ECSU and at SUNY New Paltz. She received her PhD in Statistics from the University of Connecticut, Storrs in 2015 (dissertation title: Model-based Clustering of Incomplete Data).



Klappentext

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before.

This book provides the tools needed to thrive in today's big data world. The author demonstrates how to leverage a company's existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will learn data mining by doing data mining. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining.

Inhalt
PREFACE xi

CHAPTER 1 AN INTRODUCTION TO DATA MINING 1

1.1 What is Data Mining? 1

1.2 Wanted: Data Miners 2

1.3 The Need for Human Direction of Data Mining 3

1.4 The Cross-Industry Standard Practice for Data Mining 4

1.4.1 Crisp-DM: The Six Phases 5

1.5 Fallacies of Data Mining 6

1.6 What Tasks Can Data Mining Accomplish? 8

1.6.1 Description 8

1.6.2 Estimation 8

1.6.3 Prediction 10

1.6.4 Classification 10

1.6.5 Clustering 12

1.6.6 Association 14

References 14

Exercises 15

CHAPTER 2 DATA PREPROCESSING 16

2.1 Why do We Need to Preprocess the Data? 17

2.2 Data Cleaning 17

2.3 Handling Missing Data 19

2.4 Identifying Misclassifications 22

2.5 Graphical Methods for Identifying Outliers 22

2.6 Measures of Center and Spread 23

2.7 Data Transformation 26

2.8 Min-Max Normalization 26

2.9 Z-Score Standardization 27

2.10 Decimal Scaling 28

2.11 Transformations to Achieve Normality 28

2.12 Numerical Methods for Identifying Outliers 35

2.13 Flag Variables 36

2.14 Transforming Categorical Variables into Numerical Variables 37

2.15 Binning Numerical Variables 38

2.16 Reclassifying Categorical Variables 39

2.17 Adding an Index Field 39

2.18 Removing Variables that are Not Useful 39

2.19 Variables that Should Probably Not Be Removed 40

2.20 Removal of Duplicate Records 41

2.21 A Word About ID Fields 41

The R Zone 42

References 48

Exercises 48

Hands-On Analysis 50

CHAPTER 3 EXPLORATORY DATA ANALYSIS 51

3.1 Hypothesis Testing Versus Exploratory Data Analysis 51

3.2 Getting to Know the Data Set 52

3.3 Exploring Categorical Variables 55

3.4 Exploring Numeric Variables 62

3.5 Exploring Multivariate Relationships 69

3.6 Selecting Interesting Subsets of the Data for Further Investigation 71

3.7 Using EDA to Uncover Anomalous Fields 71

3.8 Binning Based on Predictive Value 72

3.9 Deriving New Variables: Flag Variables 74

3.10 Deriving New Variables: Numerical Variables 77

3.11 Using EDA to Investigate Correlated Predictor Variables 77

3.12 Summary 80

The R Zone 82

Reference 88

Exercises 88

Hands-On Analysis 89

CHAPTER 4 UNIVARIATE STATISTICAL ANALYSIS 91

4.1 Data Mining Tasks in Discovering Knowledge in Data 91

4.2 Statistical Approaches to Estimation and Prediction 92

4.3 Statistical Inference 93

4.4 How Confident are We in Our Estimates? 94

4.5 Confidence Interval Estimation of the Mean 95

4.6 How to Reduce the Margin of Error 97

4.7 Confidence Interval Estimation of the Proportion 98

4.8 Hypothesis Testing for the Mean 99

4.9 Assessing the Strength of Evidence Against the Null Hypothesis 101

4.10 Using Confidence Intervals to Perform Hypothesis Tests 102

4.11 Hypothesis Testing for the Proportion 104

The R Zone 105

Reference 106

Exercises 106

CHAPTER 5 MULTIVARIATE STATISTICS 109

5.1 Two-Sample t-Test for Difference in Means 110

5.2 Two-Sample Z-Test for Difference in Proportions 111

5.3 Test for Homogeneity of Proportions 112

5.4 Chi-Square Test for Goodness of Fit of Multinomial Data 114

5.5 Analysis of Variance 115

5.6 Regression Analysis 118

5.7 Hypothesis Testing in Regression 122

5.8 Measuring the Quality of a Regression Model 123

5.9 Dangers of Extrapolation 123

5.10 Confidence Intervals for the Mean Value of y Given x 125

5.11 Prediction Intervals for a Randomly Chosen Value of y Given x 125

5.12 Multiple Regression 126

5.13 Verifying Model Assumptions 127

The R Zone 131

Reference 135

Exercises 135

Hands-On Analysis 136

CHAPTER 6 PREPARING TO MODEL THE DATA 138

6.1 Supervised Versus Unsupervised Methods 138

6.2 Statistical Methodology and Data Mining Methodology 139

6.3 Cross-Validation 139

6.4 Overfitting 141

6.5 BIASVariance Trade-Off 142

6.6 Balancing the Training Data Set 144

6.7 Establishing Baseline Performance 145

The R Zone 146

Reference 147

Exercises 147

CHAPTER 7 k-NEAREST NEIGHBOR ALGORITHM 149

7.1 Classification Task 149

7.2 k-Nearest Neighbor Algorithm 150

7.3 Distance Function 153

7.4 Combination Function 156

7.4.1 Simple Unweighted Voting 156

7.4.2 Weighted Voting 156

7.5 Quantifying Attribute Relevance: Stretching the Axes 158

7.6 Database Considerations 158

7.7 k-Nearest Neighbor Algorithm for Estimation and Prediction 159

7.8 Choosing k 160

7.9 Application of k-Neare...

Produktinformationen

Titel: Discovering Knowledge in Data
Untertitel: An Introduction to Data Mining
Autor:
EAN: 9781118873588
ISBN: 978-1-118-87358-8
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (pdf)
Herausgeber: Wiley
Genre: Informatik
Anzahl Seiten: 336
Veröffentlichung: 27.05.2014
Jahr: 2014
Auflage: 2. Aufl.
Untertitel: Englisch
Dateigrösse: 15.4 MB