Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining

Index

Accuracy, 9, 158–165, 167

Agglomerative hierarchical clustering, 111–120, 154

adjusting cut-off distances, 116

creating clusters, 114–116

example, 116–120

grouping process, 111–113

Aggregate table, 39

Aggregation, 31

Alternative hypothesis, 73–74

Anomaly detection, 211

Artificial neural network, see Neural network

Association rules, see Associative rules

Associative rules, 129–139

antecedent, 134

confidence, 134–135

consequence, 134

example, 137–139, 230

extracting rules, 132–137

grouping, 130–132

lift, 135–137

support, 134

Analysis of variance, see One-way analysis of variance

Average, see Mean

Bagging, 168

Bar chart, 41

Bin, 30

Binary, see Variable, binary

Binning, 30

Black-box, 197

Boosting, 168

Box plots, 25, 45–46, 52, 233

Box-and-whisker plots, see Box plots

Budget, 12, 14–15

Business analyst, 10

Case study, 12

Central limits theorem, 63

Central tendency, 55–57, 96

Charts, see Graphs

Chi-square, 39, 67, 82–84, 91

critical value, 83–84

degrees of freedom, 84

distribution, 243

expected frequencies, 83

observed frequencies, 83

Churn analysis, 210

Claim, 72

Classification, 158–162

Classification and regression tree (CART), see Decision trees

Classification models, 158, 182, 199, 202, 233

Classification trees, 181–184, 203

Cleaning, 24–26, 32, 219–220

Clustering, 25, 110–129, 168

agglomerative hierarchical clustering, 111–120, 154

bottom-up, 111

hierarchical, 110

k-means clustering, 120–129, 154

nonhierarchical, 110, 120

outlier detection, 25

top-down, 120

Common subsets, ...