Details

Discovering Knowledge in Data

An Introduction to Data Mining
Wiley Series on Methods and Applications in Data Mining 2. Aufl.

von: Daniel T. Larose, Chantal D. Larose
83,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	02.06.2014
ISBN/EAN:	9781118873571
Sprache:	englisch
Anzahl Seiten:	336

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Beschreibungen

Titelbeschreibung

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. <ul> <li>The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.</li> <li>Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization</li> <li>Offers extensive coverage of the R statistical programming language</li> <li>Contains 280 end-of-chapter exercises</li> <li>Includes a companion website for university instructors who adopt the book</li> </ul>

Inhaltsverzeichnis

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 BIAS–Variance 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-Nearest Neighbor Algorithm Using IBM/SPSS Modeler 160 The R Zone 162 Exercises 163 Hands-On Analysis 164 Chapter 8 Decision Trees 165 8.1 What is a Decision Tree? 165 8.2 Requirements for Using Decision Trees 167 8.3 Classification and Regression Trees 168 8.4 C4.5 Algorithm 174 8.5 Decision Rules 179 8.6 Comparison of the C5.0 and Cart Algorithms Applied to Real Data 180 The R Zone 183 References 184 Exercises 185 Hands-On Analysis 185 Chapter 9 Neural Networks 187 9.1 Input and Output Encoding 188 9.2 Neural Networks for Estimation and Prediction 190 9.3 Simple Example of a Neural Network 191 9.4 Sigmoid Activation Function 193 9.5 Back-Propagation 194 9.5.1 Gradient Descent Method 194 9.5.2 Back-Propagation Rules 195 9.5.3 Example of Back-Propagation 196 9.6 Termination Criteria 198 9.7 Learning Rate 198 9.8 Momentum Term 199 9.9 Sensitivity Analysis 201 9.10 Application of Neural Network Modeling 202 The R Zone 204 References 207 Exercises 207 Hands-On Analysis 207 Chapter 10 Hierarchical and K-Means Clustering 209 10.1 The Clustering Task 209 10.2 Hierarchical Clustering Methods 212 10.3 Single-Linkage Clustering 213 10.4 Complete-Linkage Clustering 214 10.5 k-Means Clustering 215 10.6 Example of k-Means Clustering at Work 216 10.7 Behavior of MSB, MSE, and PSEUDO-F as the k-Means Algorithm Proceeds 219 10.8 Application of k-Means Clustering Using SAS Enterprise Miner 220 10.9 Using Cluster Membership to Predict Churn 223 The R Zone 224 References 226 Exercises 226 Hands-On Analysis 226 Chapter 11 Kohonen Networks 228 11.1 Self-Organizing Maps 228 11.2 Kohonen Networks 230 11.2.1 Kohonen Networks Algorithm 231 11.3 Example of a Kohonen Network Study 231 11.4 Cluster Validity 235 11.5 Application of Clustering Using Kohonen Networks 235 11.6 Interpreting the Clusters 237 11.6.1 Cluster Profiles 240 11.7 Using Cluster Membership as Input to Downstream Data Mining Models 242 The R Zone 243 References 245 Exercises 245 Hands-On Analysis 245 Chapter 12 Association Rules 247 12.1 Affinity Analysis and Market Basket Analysis 247 12.1.1 Data Representation for Market Basket Analysis 248 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property 249 12.3 How Does the a Priori Algorithm Work? 251 12.3.1 Generating Frequent Itemsets 251 12.3.2 Generating Association Rules 253 12.4 Extension from Flag Data to General Categorical Data 255 12.5 Information-Theoretic Approach: Generalized Rule Induction Method 256 12.5.1 J-Measure 257 12.6 Association Rules are Easy to do Badly 258 12.7 How Can We Measure the Usefulness of Association Rules? 259 12.8 Do Association Rules Represent Supervised or Unsupervised Learning? 260 12.9 Local Patterns Versus Global Models 261 The R Zone 262 References 263 Exercises 263 Hands-On Analysis 264 Chapter 13 Imputation of Missing Data 266 13.1 Need for Imputation of Missing Data 266 13.2 Imputation of Missing Data: Continuous Variables 267 13.3 Standard Error of the Imputation 270 13.4 Imputation of Missing Data: Categorical Variables 271 13.5 Handling Patterns in Missingness 272 The R Zone 273 Reference 276 Exercises 276 Hands-On Analysis 276 Chapter 14 Model Evaluation Techniques 277 14.1 Model Evaluation Techniques for the Description Task 278 14.2 Model Evaluation Techniques for the Estimation and Prediction Tasks 278 14.3 Model Evaluation Techniques for the Classification Task 280 14.4 Error Rate, False Positives, and False Negatives 280 14.5 Sensitivity and Specificity 283 14.6 Misclassification Cost Adjustment to Reflect Real-World Concerns 284 14.7 Decision Cost/Benefit Analysis 285 14.8 Lift Charts and Gains Charts 286 14.9 Interweaving Model Evaluation with Model Building 289 14.10 Confluence of Results: Applying a Suite of Models 290 The R Zone 291 Reference 291 Exercises 291 Hands-On Analysis 291 Appendix: Data Summarization and Visualization 294 Index 309

Autorenportrait

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).