Data Mining and Predictive Analytics

Name: Data Mining and Predictive Analytics
Brand: Wiley
Price: 111.99 EUR
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Daniel T. Larose Chantal D. Larose(Autor*in)

Wiley (Verlag)

2. Auflage

Erschienen am 19. Februar 2015

824 Seiten

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Inhalt

Cover
Contents
Preface
Acknowledgments
Part I Data Preparation
Chapter 1 An Introduction to Data Mining and Predictive Analytics
1.1 What is Data Mining? What is Predictive Analytics?
1.2 Wanted: Data Miners
1.3 The Need for Human Direction of Data Mining
1.4 The Cross-Industry Standard Process for Data Mining: CRISP-DM
1.4.1 CRISP-DM: The Six Phases
1.5 Fallacies of Data Mining
1.6 What Tasks Can Data Mining Accomplish
1.6.1 Description
1.6.2 Estimation
1.6.3 Prediction
1.6.4 Classification
1.6.5 Clustering
1.6.6 Association
The R Zone
R References
Exercises
Chapter 2 Data Preprocessing
2.1 Why do We Need to Preprocess the Data?
2.2 Data Cleaning
2.3 Handling Missing Data
2.4 Identifying Misclassifications
2.5 Graphical Methods for Identifying Outliers
2.6 Measures of Center and Spread
2.7 Data Transformation
2.8 Min-Max Normalization
2.9 Z-Score Standardization
2.10 Decimal Scaling
2.11 Transformations to Achieve Normality
2.12 Numerical Methods for Identifying Outliers
2.13 Flag Variables
2.14 Transforming Categorical Variables into Numerical Variables
2.15 Binning Numerical Variables
2.16 Reclassifying Categorical Variables
2.17 Adding an Index Field
2.18 Removing Variables that are not Useful
2.19 Variables that Should Probably not be Removed
2.20 Removal of Duplicate Records
2.21 A Word About ID Fields
The R Zone
R Reference
Exercises
Chapter 3 Exploratory Data Analysis
3.1 Hypothesis Testing Versus Exploratory Data Analysis
3.2 Getting to Know the Data Set
3.3 Exploring Categorical Variables
3.4 Exploring Numeric Variables
3.5 Exploring Multivariate Relationships
3.6 Selecting Interesting Subsets of the Data for Further Investigation
3.7 Using EDA to Uncover Anomalous Fields
3.8 Binning Based on Predictive Value
3.9 Deriving New Variables: Flag Variables
3.10 Deriving New Variables: Numerical Variables
3.11 Using EDA to Investigate Correlated Predictor Variables
3.12 Summary of Our EDA
The R Zone
R References
Exercises
Chapter 4 Dimension-Reduction Methods
4.1 Need for Dimension-Reduction in Data Mining
4.2 Principal Components Analysis
4.3 Applying PCA to the Houses Data Set
4.4 How Many Components Should We Extract?
4.4.1 The Eigenvalue Criterion
4.4.2 The Proportion of Variance Explained Criterion
4.4.3 The Minimum Communality Criterion
4.4.4 The Scree Plot Criterion
4.5 Profiling the Principal Components
4.6 Communalities
4.6.1 Minimum Communality Criterion
4.7 Validation of the Principal Components
4.8 Factor Analysis
4.9 Applying Factor Analysis to the Adult Data Set
4.10 Factor Rotation
4.11 User-Defined Composites
4.12 An Example of a User-Defined Composite
The R Zone
R References
Exercises
Part II Statistical Analysis
Chapter 5 Univariate Statistical Analysis
5.1 Data Mining Tasks in Discovering Knowledge in Data
5.2 Statistical Approaches to Estimation and Prediction
5.3 Statistical Inference
5.4 How Confident are We in Our Estimates?
5.5 Confidence Interval Estimation of the Mean
5.6 How to Reduce the Margin of Error
5.7 Confidence Interval Estimation of the Proportion
5.8 Hypothesis Testing for the Mean
5.9 Assessing the Strength of Evidence Against the Null Hypothesis
5.10 Using Confidence Intervals to Perform Hypothesis Tests
5.11 Hypothesis Testing for the Proportion
Reference
The R Zone
R Reference
Exercises
Chapter 6 Multivariate Statistics
6.1 Two-Sample t-Test for Difference in Means
6.2 Two-Sample Z-Test for Difference in Proportions
6.3 Test for the Homogeneity of Proportions
6.4 Chi-Square Test for Goodness of Fit of Multinomial Data
6.5 Analysis of Variance
Reference
The R Zone
R Reference
Exercises
Chapter 7 Preparing to Model the Data
7.1 Supervised Versus Unsupervised Methods
7.2 Statistical Methodology and Data Mining Methodology
7.3 Cross-Validation
7.4 Overfitting
7.5 Bias-Variance Trade-Off
7.6 Balancing the Training Data Set
7.7 Establishing Baseline Performance
The R Zone
R Reference
Exercises
Chapter 8 Simple Linear Regression
8.1 An Example of Simple Linear Regression
8.1.1 The Least-Squares Estimates
8.2 Dangers of Extrapolation
8.3 How Useful is the Regression? The Coefficient of Determination, r2
8.4 Standard Error of the Estimate, s
8.5 Correlation Coefficient r
8.6 Anova Table for Simple Linear Regression
8.7 Outliers, High Leverage Points, and Influential Observations
8.8 Population Regression Equation
8.9 Verifying the Regression Assumptions
8.10 Inference in Regression
8.11 t-Test for the Relationship Between x and y
8.12 Confidence Interval for the Slope of the Regression Line
8.13 Confidence Interval for the Correlation Coefficient ?
8.14 Confidence Interval for the Mean Value of y Given x
8.15 Prediction Interval for a Randomly Chosen Value of y Given x
8.16 Transformations to Achieve Linearity
8.17 Box-Cox Transformations
The R Zone
R References
Exercises
Chapter 9 Multiple Regression and Model Building
9.1 An Example of Multiple Regression
9.2 The Population Multiple Regression Equation
9.3 Inference in Multiple Regression
9.3.1 The t-Test for the Relationship Between y and xi
9.3.2 t-Test for Relationship Between Nutritional Rating and Sugars
9.3.3 t-Test for Relationship Between Nutritional Rating and Fiber Content
9.3.4 The F-Test for the Significance of the Overall Regression Model
9.3.5 F-Test for Relationship between Nutritional Rating and {Sugar and Fiber}, Taken Together
9.3.6 The Confidence Interval for a Particular Coefficient, ßi
9.3.7 The Confidence Interval for the Mean Value of y, Given x1, x2, ..., xm
9.3.8 The Prediction Interval for a Randomly Chosen Value of y, Given x1, x2, ..., xm
9.4 Regression with Categorical Predictors, Using Indicator Variables
9.5 Adjusting R2: Penalizing Models for Including Predictors that are not Useful
9.6 Sequential Sums of Squares
9.7 Multicollinearity
9.8 Variable Selection Methods
9.8.1 The Partial F-Test
9.8.2 The Forward Selection Procedure
9.8.3 The Backward Elimination Procedure
9.8.4 The Stepwise Procedure
9.8.5 The Best Subsets Procedure
9.8.6 The All-Possible-Subsets Procedure
9.9 Gas Mileage Data Set
9.10 An Application of Variable Selection Methods
9.10.1 Forward Selection Procedure Applied to the Gas Mileage Data Set
9.10.2 Backward Elimination Procedure Applied to the Gas Mileage Data Set
9.10.3 The Stepwise Selection Procedure Applied to the Gas Mileage Data Set
9.10.4 Best Subsets Procedure Applied to the Gas Mileage Data Set
9.10.5 Mallows' Cp Statistic
9.11 Using the Principal Components as Predictors in Multiple Regression
The R Zone
R References
Exercises
Part III Classification
Chapter 10 k-Nearest Neighbor Algorithm
10.1 Classification Task
10.2 k-Nearest Neighbor Algorithm
10.3 Distance Function
10.4 Combination Function
10.4.1 Simple Unweighted Voting
10.4.2 Weighted Voting
10.5 Quantifying Attribute Relevance: Stretching the Axes
10.6 Database Considerations
10.7 k-Nearest Neighbor Algorithm for Estimation and Prediction
10.8 Choosing k
10.9 Application of k-Nearest Neighbor Algorithm Using IBM/SPSS Modeler
The R Zone
R References
Exercises
Chapter 11 Decision Trees
11.1 What is a Decision Tree?
11.2 Requirements for Using Decision Trees
11.3 Classification and Regression Trees
11.4 C4.5 Algorithm
11.5 Decision Rules
11.6 Comparison of the C5.0 and CART Algorithms Applied to Real Data
The R Zone
R References
Exercises
Chapter 12 Neural Networks
12.1 Input and Output Encoding
12.2 Neural Networks for Estimation and Prediction
12.3 Simple Example of a Neural Network
12.4 Sigmoid Activation Function
12.5 Back-Propagation
12.6 Gradient-Descent Method
12.7 Back-Propagation Rules
12.8 Example of Back-Propagation
12.9 Termination Criteria
12.10 Learning Rate
12.11 Momentum Term
12.12 Sensitivity Analysis
12.13 Application of Neural Network Modeling
The R Zone
R References
Exercises
Chapter 13 Logistic Regression
13.1 Simple Example of Logistic Regression
13.2 Maximum Likelihood Estimation
13.3 Interpreting Logistic Regression Output
13.4 Inference: are the Predictors Significant?
13.5 Odds Ratio and Relative Risk
13.6 Interpreting Logistic Regression for a Dichotomous Predictor
13.7 Interpreting Logistic Regression for a Polychotomous Predictor
13.8 Interpreting Logistic Regression for a Continuous Predictor
13.9 Assumption of Linearity
13.10 Zero-Cell Problem
13.11 Multiple Logistic Regression
13.12 Introducing Higher Order Terms to Handle Nonlinearity
13.13 Validating the Logistic Regression Model
13.14 WEKA: Hands-On Analysis Using Logistic Regression
The R Zone
R References
Exercises
Chapter 14 Naïve Bayes and Bayesian Networks
14.1 Bayesian Approach
14.2 Maximum a Posteriori (Map) Classification
14.3 Posterior Odds Ratio
14.4 Balancing the Data
14.5 Naïve Bayes Classification
14.6 Interpreting the Log Posterior Odds Ratio
14.7 Zero-Cell Problem
14.8 Numeric Predictors for Naïve Bayes Classification
14.9 WEKA: Hands-on Analysis Using Naïve Bayes
14.10 Bayesian Belief Networks
14.11 Clothing Purchase Example
14.12 Using the Bayesian Network to Find Probabilities
14.12.1 WEKA: Hands-on Analysis Using Bayes Net
The R Zone
R References
Exercises
Chapter 15 Model Evaluation Techniques
15.1 Model Evaluation Techniques for the Description Task
15.2 Model Evaluation Techniques for the Estimation and Prediction Tasks
15.3 Model Evaluation Measures for the Classification Task
15.4 Accuracy and Overall Error Rate
15.5 Sensitivity and Specificity
15.6 False-Positive Rate and False-Negative Rate
15.7 Proportions of True Positives, True Negatives, False Positives, and False Negatives
15.8 Misclassification Cost Adjustment to Reflect Real-World Concerns
15.9 Decision Cost/Benefit Analysis
15.10 Lift Charts and Gains Charts
15.11 Interweaving Model Evaluation with Model Building
15.12 Confluence of Results: Applying a Suite of Models
The R Zone
R References
Exercises
Chapter 16 Cost-Benefit Analysis Using Data-Driven Costs
16.1 Decision Invariance Under Row Adjustment
16.2 Positive Classification Criterion
16.3 Demonstration of the Positive Classification Criterion
16.4 Constructing the Cost Matrix
16.5 Decision Invariance Under Scaling
16.6 Direct Costs and Opportunity Costs
16.7 Case Study: Cost-Benefit Analysis Using Data-Driven Misclassification Costs
16.8 Rebalancing as a Surrogate for Misclassification Costs
The R Zone
R References
Exercises
Chapter 17 COST-BENEFIT ANALYSIS FOR TRINARY AND k-NARY CLASSIFICATION MODELS
17.1 Classification Evaluation Measures for a Generic Trinary Target
17.2 Application of Evaluation Measures for Trinary Classification to the Loan Approval Problem
17.3 Data-Driven Cost-Benefit Analysis for Trinary Loan Classification Problem
17.4 Comparing Cart Models with and without Data-Driven Misclassification Costs
17.5 Classification Evaluation Measures for a Generic k-Nary Target
17.6 Example of Evaluation Measures and Data-Driven Misclassification Costs for k-Nary Classification
The R Zone
R References
Exercises
Chapter 18 Graphical Evaluation of Classification Models
18.1 Review of Lift Charts and Gains Charts
18.2 Lift Charts and Gains Charts Using Misclassification Costs
18.3 Response Charts
18.4 Profits Charts
18.5 Return on Investment (ROI) Charts
The R Zone
R References
Exercises
Part IV Clustering
Chapter 19 HIERARCHICAL AND k-MEANS CLUSTERING
19.1 The Clustering Task
19.2 Hierarchical Clustering Methods
19.3 Single-Linkage Clustering
19.4 Complete-Linkage Clustering
19.5 k-Means Clustering
19.6 Example of k-Means Clustering at Work
19.7 Behavior of MSB, MSE, and Pseudo-F as the k-Means Algorithm Proceeds
19.8 Application of k-Means Clustering Using SAS Enterprise Miner
19.9 Using Cluster Membership to Predict Churn
The R Zone
R References
Exercises
Chapter 20 Kohonen Networks
20.1 Self-Organizing Maps
20.2 Kohonen Networks
20.3 Example of a Kohonen Network Study
20.4 Cluster Validity
20.5 Application of Clustering Using Kohonen Networks
20.6 Interpreting The Clusters
20.6.1 Cluster Profiles
20.7 Using Cluster Membership as Input to Downstream Data Mining Models
The R Zone
R References
Exercises
Chapter 21 Birch Clustering
21.1 Rationale for Birch Clustering
21.2 Cluster Features
21.3 Cluster Feature Tree
21.4 Phase 1: Building the CF Tree
21.5 Phase 2: Clustering the Sub-Clusters
21.6 Example of Birch Clustering, Phase 1: Building the CF Tree
21.7 Example of Birch Clustering, Phase 2: Clustering the Sub-Clusters
21.8 Evaluating the Candidate Cluster Solutions
21.9 Case Study: Applying Birch Clustering to the Bank Loans Data Set
21.9.1 Case Study Lesson One: Avoid Highly Correlated Inputs to Any Clustering Algorithm
21.9.2 Case Study Lesson Two: Different Sortings May Lead to Different Numbers of Clusters
The R Zone
R References
Exercises
Chapter 22 Measuring Cluster Goodness
22.1 Rationale for Measuring Cluster Goodness
22.2 The Silhouette Method
22.3 Silhouette Example
22.4 Silhouette Analysis of the IRIS Data Set
22.5 The Pseudo-F Statistic
22.6 Example of the Pseudo-F Statistic
22.7 Pseudo-F Statistic Applied to the IRIS Data Set
22.8 Cluster Validation
22.9 Cluster Validation Applied to the Loans Data Set
The R Zone
R References
Exercises
Part V Association Rules
Chapter 23 Association Rules
23.1 Affinity Analysis and Market Basket Analysis
23.1.1 Data Representation for Market Basket Analysis
23.2 Support, Confidence, Frequent Itemsets, and the a Priori Property
23.3 How Does the a Priori Algorithm Work (Part 1)? Generating Frequent Itemsets
23.4 How Does the a Priori Algorithm Work (Part 2)? Generating Association Rules
23.5 Extension from Flag Data to General Categorical Data
23.6 Information-Theoretic Approach: Generalized Rule Induction Method
23.6.1 J-Measure
23.7 Association Rules are Easy to do Badly
23.8 How can we Measure the Usefulness of Association Rules?
23.9 Do Association Rules Represent Supervised or Unsupervised Learning?
23.10 Local Patterns Versus Global Models
The R Zone
R References
Exercises
Part VI Enhancing Model Performance
Chapter 24 Segmentation Models
24.1 The Segmentation Modeling Process
24.2 Segmentation Modeling Using EDA to Identify the Segments
24.3 Segmentation Modeling using Clustering to Identify the Segments
The R Zone
R References
Exercises
Chapter 25 Ensemble Methods: Bagging and Boosting
25.1 Rationale for Using an Ensemble of Classification Models
25.2 Bias, Variance, and Noise
25.3 When to Apply, and not to apply, Bagging
25.4 Bagging
25.5 Boosting
25.6 Application of Bagging and Boosting Using IBM/SPSS Modeler
References
The R Zone
R Reference
Exercises
Chapter 26 Model Voting and Propensity Averaging
26.1 Simple Model Voting
26.2 Alternative Voting Methods
26.3 Model Voting Process
26.4 An Application of Model Voting
26.5 What is Propensity Averaging?
26.6 Propensity Averaging Process
26.7 An Application of Propensity Averaging
The R Zone
R References
Exercises
Part VII Further Topics
Chapter 27 Genetic Algorithms
27.1 Introduction To Genetic Algorithms
27.2 Basic Framework of a Genetic Algorithm
27.3 Simple Example of a Genetic Algorithm at Work
27.3.1 First Iteration
27.3.2 Second Iteration
27.4 Modifications and Enhancements: Selection
27.5 Modifications and Enhancements: Crossover
27.5.1 Multi-Point Crossover
27.5.2 Uniform Crossover
27.6 Genetic Algorithms for Real-Valued Variables
27.6.1 Single Arithmetic Crossover
27.6.2 Simple Arithmetic Crossover
27.6.3 Whole Arithmetic Crossover
27.6.4 Discrete Crossover
27.6.5 Normally Distributed Mutation
27.7 Using Genetic Algorithms to Train a Neural Network
27.8 WEKA: Hands-On Analysis Using Genetic Algorithms
The R Zone
R References
Exercises
Chapter 28 Imputation of Missing Data
28.1 Need for Imputation of Missing Data
28.2 Imputation of Missing Data: Continuous Variables
28.3 Standard Error of the Imputation
28.4 Imputation of Missing Data: Categorical Variables
28.5 Handling Patterns in Missingness
Reference
The R Zone
R References
Exercises
Part VIII Case Study: Predicting Response to Direct-Mail Marketing
Chapter 29 Case Study, Part 1: Business Understanding, Data Preparation, and EDA
29.1 Cross-Industry Standard Practice for Data Mining
29.2 Business Understanding Phase
29.3 Data Understanding Phase, Part 1: Getting a Feel for the Data Set
29.4 Data Preparation Phase
29.4.1 Negative Amounts Spent?
29.4.2 Transformations to Achieve Normality or Symmetry
29.4.3 Standardization
29.4.4 Deriving New Variables
29.5 Data Understanding Phase, Part 2: Exploratory Data Analysis
29.5.1 Exploring the Relationships between the Predictors and the Response
29.5.2 Investigating the Correlation Structure among the Predictors
29.5.3 Importance of De-Transforming for Interpretation
Chapter 30 Case Study, Part 2: Clustering and Principal Components Analysis
30.1 Partitioning the Data
30.1.1 Validating the Partition
30.2 Developing the Principal Components
30.3 Validating the Principal Components
30.4 Profiling the Principal Components
30.5 Choosing the Optimal Number of Clusters Using Birch Clustering
30.6 Choosing the Optimal Number of Clusters Using k-Means Clustering
30.7 Application of k-Means Clustering
30.8 Validating the Clusters
30.9 Profiling the Clusters
Chapter 31 Case Study, Part 3: Modeling And Evaluation For Performance And Interpretability
31.1 Do you Prefer the Best Model Performance, or a Combination of Performance and Interpretability?
31.2 Modeling and Evaluation Overview
31.3 Cost-Benefit Analysis Using Data-Driven Costs
31.3.1 Calculating Direct Costs
31.4 Variables to be Input to the Models
31.5 Establishing the Baseline Model Performance
31.6 Models that use Misclassification Costs
31.7 Models that Need Rebalancing as a Surrogate for Misclassification Costs
31.8 Combining Models Using Voting and Propensity Averaging
31.9 Interpreting the Most Profitable Model
Chapter 32 Case Study, Part 4: Modeling and Evaluation for High Performance Only
32.1 Variables to be Input to the Models
32.2 Models that use Misclassification Costs
32.3 Models that Need Rebalancing as a Surrogate for Misclassification Costs
32.4 Combining Models using Voting and Propensity Averaging
32.5 Lessons Learned
32.6 Conclusions
Part 1: Summarization 1: Building Blocks of Data Analysis
Part 2: Visualization: Graphs and Tables for Summarizing and Organizing Data
Part 3: Summarization 2: Measures of Center, Variability, and Position
Part 4: Summarization and Visualization of Bivariate Relationships
Index
EULA

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