Data Mining and Predictive Analytics.

Format:

Book

Author/Creator:

Larose, Daniel T.

Contributor:

Larose, Chantal D.

Series:

Wiley Series on Methods and Applications in Data Mining Ser.

Language:

English

Subjects (All):

Data mining.

Prediction theory.

Physical Description:

1 online resource (827 pages)

Edition:

2nd ed.

Place of Publication:

New York : John Wiley & Sons, Incorporated, 2015.

Summary:

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified "white box" approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics, Second Edition: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant.com, with exclusive password-protected instructor content Data Mining and Predictive Analytics, Second Edition will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Contents:

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

R Reference

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

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

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

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

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

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 ρ

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

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, βi

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Larose, Daniel T. Data Mining and Predictive Analytics

ISBN:

9781118868676

OCLC:

907093982