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

by Myatt, Glenn J.; Johnson, Wayne P.

• ISBN13: 9780470222805
• ISBN10: 0470222808
• Edition: 1st
• Format: Paperback
• Copyright: 2009-02-03
• Publisher: Wiley

Summary

This second installment in the Making Sense of Data series continues to explore a diverse range 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 engineering, finance, and the social sciences.

Author Biography

Glenn J. Myatt, PhD, is cofounder of Leadscope, Inc. and a Partner of Myatt & Johnson, Inc., a consulting company that focuses on business intelligence application development delivered through the Internet. Dr. Myatt is the author of Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining, also published by Wiley. WAYNE P. JOHNSON, MSc., is cofounder of Leadscope, Inc. and a Partner of Myatt & Johnson, Inc. Mr. Johnson has over two decades of experience in the design and development of large software systems, and his current professional interests include human–computer interaction, information visualization, and methodologies for contextual inquiry.

Table of Contents

Preface	p. xi
Introduction	p. 1
Overview	p. 1
Definition	p. 1
Preparation	p. 2
Overview	p. 2
Accessing Tabular Data	p. 3
Accessing Unstructured Data	p. 3
Understanding the Variables and Observations	p. 3
Data Cleaning	p. 6
Transformation	p. 7
Variable Reduction	p. 9
Segmentation	p. 10
Preparing Data to Apply	p. 10
Analysis	p. 11
Data Mining Tasks	p. 11
Optimization	p. 12
Evaluation	p. 12
Model Forensics	p. 13
Deployment	p. 13
Outline of Book	p. 14
Overview	p. 14
Data Visualization	p. 14
Clustering	p. 15
Predictive Analytics	p. 15
Applications	p. 16
Software	p. 16
Summary	p. 16
Further Reading	p. 17
Data Visualization	p. 19
Overview	p. 19
Visualization Design Principles	p. 20
General Principles	p. 20
Graphics Design	p. 20
Anatomy of a Graph	p. 28
Tables	p. 32
Simple Tables	p. 32
Summary Tables	p. 32
Two-Way Contingency Tables	p. 34
Supertables	p. 34
Univariate Data Visualization	p. 36
Bar Chart	p. 36
Histograms	p. 37
Frequency Polygram	p. 41
Box Plots	p. 41
Dot Plot	p. 43
Stem-and Leaf Plot	p. 44
Quantile Plot	p. 46
Quantile-Quantile Plot	p. 48
Bivariate Data Visualization	p. 49
Scatterplot	p. 49
Multivariate Data Visualization	p. 50
Histogram Matrix	p. 52
Scatterplot Matrix	p. 54
Multiple Box Plot	p. 56
Trellis Plot	p. 56
Visualizing Groups	p. 59
Dendrograms	p. 59
Decision Trees	p. 60
Cluster Image Maps	p. 60
Dynamic Techniques	p. 63
Overview	p. 63
Data Brushing	p. 64
Nearness Selection	p. 65
Sorting and Rearranging	p. 65
Searching and Filtering	p. 65
Summary	p. 65
Further Reading	p. 66
Clustering	p. 67
Overview	p. 67
Distance Measures	p. 75
Overview	p. 75
Numeric Distance Measures	p. 77
Binary Distance Measures	p. 79
Mixed Variables	p. 84
Other Measures	p. 86
Agglomerative Hierarchical Clustering	p. 87
Overview	p. 87
Simple Linkage	p. 87
Complete Linkage	p. 92
Average Linkage	p. 93
Other Methods	p. 96
Selecting Groups	p. 96
Partitioned-Based Clustering	p. 98
Overview	p. 98
k-Means	p. 98
Worked Example	p. 100
Miscellaneous Partitioned-Based Clustering	p. 101
Fuzzy Clustering	p. 103
Overview	p. 103
Fuzzy k-Means	p. 103
Worked Examples	p. 104
Summary	p. 109
Further Reading	p. 110
Predictive Analytics	p. 111
Overview	p. 111
Predictive Modeling	p. 111
Testing Model Accuracy	p. 116
Evaluating Regression Models' Predictive Accuracy	p. 117
Evaluating Classification Models' Predictive Accuracy	p. 119
Evaluating Binary Models' Predictive Accuracy	p. 120
ROC Charts	p. 122
Lift Chart	p. 124
Principal Component Analysis	p. 126
Overview	p. 126
Principal Components	p. 126
Generating Principal Components	p. 127
Interpretation of Principal Components	p. 128
Multiple Linear Regression	p. 130
Overview	p. 130
Generating Models	p. 130
Prediction	p. 136
Analysis of Residuals	p. 136
Standard Error	p. 139
Coefficient of Multiple Determination	p. 140
Testing the Model Significance	p. 142
Selecting and Transforming Variables	p. 143
Discriminant Analysis	p. 145
Overview	p. 145
Discriminant Function	p. 146
Discriminant Analysis Example	p. 146
Logistic Regression	p. 151
Overview	p. 151
Logistic Regression Formula	p. 151
Estimating Coefficients	p. 153
Assessing and Optimizing Results	p. 156
Naïve Bayes Classifiers	p. 157
Overview	p. 157
Bayes Theorem and the Independence Assumption	p. 158
Independence Assumption	p. 158
Classification Process	p. 159
Summary	p. 161
Further Reading	p. 163
Applications	p. 165
	p. Overview
Sales and Marketing	p. 166
Industry-Specific Data Mining	p. 169
Finance	p. 169
Insurance	p. 171
Retail	p. 172
Telecommunications	p. 173
Manufacturing	p. 174
Entertainment	p. 175
Pharamaceuticals	p. 177
Healthcare	p. 178
micro RNA Data Analysis Case? Study	p. 181
Defining the Problem	p. 181
Preparing the Data	p. 181
Analysis	p. 183
Credit Scoring Case Study	p. 192
Defining the Problem	p. 192
Preparing the Data	p. 192
Analysis	p. 199
Deployment	p. 203
Data Mining Nontabular Data	p. 203
Overview	p. 203
Data Mining Chemical Data 203
Data Mining Text	p. 210
Further Reading	p. 213
Martices	p. 215
Overview of Matrices	p. 215
Matrix Addition	p. 215
Matrix Multiplication	p. 216
Transpose of a Matrix	p. 217
Inverse of a Matrix	p. 217
Software	p. 219
Software Overview	p. 219
Software Objectives	p. 219
Access and Installation	p. 221
User Interface Overview	p. 221
Data Preparation	p. 223
Overview	p. 223
Reading in Data	p. 224
Searching the Data	p. 225
Variable Characterization	p. 237
Removing Observations and Variables	p. 228
Clearing the Data	p. 228
Transforming the Data	p. 230
Segmentation	p. 235
Principal Component Analysis	p. 236
Tables and Graphs	p. 238
Overview	p. 238
Contingency Tables	p. 239
Summary Tables	p. 240
Graphs	p. 242
Graph Matrices	p. 246
Statistics	p. 246
Overview	p. 246
Descriptive Statistics	p. 248
Confidence Intervals	p. 248
Hypothesis Tests	p. 249
Chi-Square Test	p. 250
ANOVA	p. 251
comparative Statistics	p. 251
Grouping	p. 253
Overview	p. 253
Clustering	p. 254
Associative Rules	p. 257
Decision Trees	p. 258
Prediction	p. 261
Overview	p. 261
Linear Regression	p. 263
Discriminant Analysis	p. 265
Logistic Regression	p. 266
Naive Bayes	p. 267
kNN	p. 269
CART	p. 269
Neural Networks	p. 270
Apply Model	p. 271
Bibliography	p. 273
Index	p. 279
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