Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications

A hands–on guide to making valuable decisions from data using advanced data mining methods and techniques
This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences.
Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis:
Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces.
Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned–based clustering, and fuzzy clustering are also discussed.
Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes.
Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real–world scenarios.
Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands–on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online.
With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper–undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

Spis treści:
Preface.
1. Introduction.
1.1 Overview.
1.2 Definition.
1.3 Preparation.
1.3.1 Overview.
1.3.2 Accessing Tabular Data.
1.3.3 Accessing Unstructured Data.
1.3.4 Understanding the Variables and Observations.
1.3.5 Data Cleaning.
1.3.6 Transformation.
1.3.7 Variable Reduction.
1.3.8 Segmentation.
1.3.9 Preparing Data to Apply.
1.4 Analysis.
1.4.1 Data Mining Tasks.
1.4.2 Optimization.
1.4.3 Evaluation.
1.4.4 Model Forensics.
1.5 Deployment.
1.6 Outline of Book.
1.6.1 Overview.
1.6.2 Data Visualization.
1.6.3 Clustering.
1.6.4 Predictive Analytics.
1.6.5 Applications.
1.6.6 Software.
1.7 Summary.
1.8 Further Reading.
2. Data Visualization.
2.1 Overview.
2.2 Visualization Design Principles.
2.2.1 General Principles.
2.2.2 Graphics Design.
2.2.3 Anatomy of a Graph.
2.3 Tables.
2.3.1 Sim ple Tables.
2.3.2 Summary Tables.
2.3.3 Two–Way Contingency Tables.
2.3.4 Supertables.
2.4 Univariate Data Visualization.
2.4.1 Bar Chart.
2.4.2 Histograms.
2.4.3 Frequency Polygram.
2.4.4 Box Plots.
2.4.5 Dot Plot.
2.4.6 Stem–and–Leaf Plot .
2.4.7 Quantile Plot.
2.4.8 Quantile–Quantile Plot.
2.5 Bivariate Data Visualization.
2.5.1 Scatterplot.
2.6 Multivariate Data Visualization.
2.6.1 Histogram Matrix.
2.6.2 Scatterplot Matrix.
2.6.3 Multiple Box Plot.
2.6.4 Trellis Plot.
2.7 Visualizing Groups.
2.7.1 Dendrograms.
2.7.2 Decision Trees.
2.7.3 Cluster Image Maps.
2.8 Dynamic Techniques.
2.8.1 Overview.
2.8.2 Data Brushing.
2.8.3 Nearness Selection.
2.8.4 Sorting and Rearranging.
2.8.5 Searching and Filtering.
2.9 Summary.
2.10 Further Reading.
3. Clustering.
3.1 Overview.
3.2 Distance Measures.
3.2.1 Overview.
3.2.2 Numeric Distance Measures.
3.2.3 Binary Distance Measures.
3.3.4 Mixed Variables.
3.3.5 Others Measures.
3.3 Agglomerative Hierarchical Clustering.
3.3.1 Overview.
3.3.2 Single Linkage.
3.3.3 Complete Linkage.
3.2.4 Average Linkage.
3.3.5 Other Methods.
3.3.6 Selecting Groups.
3.4 Partitioned–Based Clustering.
3.4.1 Overview.
3.4.2 k–Means.
3.4.3 Worked Example.
3.4.4 Miscellaneous Partitioned–Based Clustering.
3.5 Fuzzy Clustering.
3.5.1 Overview.
3.5.2 Fuzzy k–Means.
3.5.3 Worked Examples.
3.6 Summary.
3.7 Further Reading.
4. Predictive Analytics.
4.1 Overview.
4.1.1 Predictive Modeling.
4.1.2 Testing Model Accuracy.
4.1.3 Evaluating Regression Models’ Predictive Accuracy.
4.1.4 Evaluating Classification Models’ Predictive Accuracy.
4.1.5 Evaluating Binary Models’ Predictive Accuracy.
4.1.6 ROC Charts.
4.1.7 Lift Chart.
4.2 Principal Component Analysis.
4.2.1 Overview.
4.2.2 Principal Components.
4.2.3 Generating Principal Components.
4.2.4 Interpretation of Principal Components.
4.3 Multiple Linear Regression.
4.3.1 Overview.
4.3.2 Generating Models.
4.3.3 Prediction.
4.3.4 Analysis of Residuals.
4.3.5 Standard Error.
4.3.6 Coefficient of Multiple Determination.
4.3.7 Testing the Model Significance.
4.3.8 Selecting and Transforming Variables.
4.4 Discriminant Analysis.
4.4.1 Overview.
4.4.2 Discriminant Function.
4.4.3 Discriminant Analysis Example.
4.5 Logistic Regression.
4.5.1 Overview.
4.5.2 Logistic Regression Formula.
4.5.3 Estimating Coefficients.
4.5.4 Assessing and Optimizing Results.
4.6 Naive Bayes Classifiers.
4.6.1 Overview.
4.6.2 Bayes Theorem and the Independence Assumption.
4.6.3 Independence Assumption.
4.6.4 Classification Process.
4.7 Summary.
4.8 Further Reading.
5. Applications.
5.1 Overview.
5.2 Sales and Marketing.
5.3 Industry–Specific Data Mining.
5.3.1 Finance.
5.3.2 Insurance.
5.3.3 Retail.
5.3.4 Telecommunications.
5.3.5 Manufacturing.
5.3.6 Entertainment.
5.3.7 Government.
5.3.8 Pharmaceuticals.
5.3.9 Healthcare.
5.4 MicroRNA Data Analysis Case Study.
5.4.1 Defining the Problem.
5.4.2 Preparing the Data.
5.4.3 Analysis.
5.5 Credit Scoring Case Study.
5.5.1 Defining the Problem.
5.5.2 Preparing the Data.
5.5.3 Analysis.
5.5.4 Deployment.
5.6 Data Mining Nontabular Data.
5.6.1 Overview.
5.6.2 Data Mining Chemical Data.
5.6.3 Data Mining Text.
5.12 Further Reading.
Appendix A. Matrices.
A.1 Overview of Matrices.
A.2 Matrix Addition.
A.3 Matrix Multiplication.
A.4 Transpose of a Matrix.
A.4 Inverse of a Matrix.
Appendix B. Software.
B.1 Software Overview.
B.1.1 Software Objectives.
B.1.2 Access and Installation.
B.1.3 User interface Overview.
B.2 Data Preparation.
B.2.1 Overview.
B.2.2 Readi