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

CONTENTS

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 Simple 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 ...