Adult data set
Allele
ANOVA table
Back-propagation
18, 176
240-241
46
250
212, 298299
68
204-206
227-234
246
74
Balancing the data
Baseball data set
Bayesian approach
Bayesian belief networks
Boltzmann selection
California data set
Case study: modeling response
to direct mail marketing
265-316
case study: business
understanding phase
building the cost/benefit table
direct mail marketing response
problem
false negative
false positive
true negative
true positive
case study: data
understanding and data
preparation phases
clothing-store data set
deriving new variables
exploring the relationship between the
predictors and the response
investigating the correlation structure
among the predictors
Microvision life style clusters
product uniformity
standardization and flag variables
transformations to achieve normality
or symmetry
case study: modeling and
evaluation phases
balancing the training data set
cluster analysis: BIRCH clustering
algorithm
cluster profiles
combining models using the mean
response probabilities
combining models: voting
comprehensive listing of input
variables
establishing base line model
performance
model collection A: using the
principle components
model collection B: non-PCA models
267-270
267-270
267
269
269
268
268
270-289
270
277-278
278-286
286-289
271
272
276-277
272-275
289-312
298-299
294-298
294-297
308-312
304-306
291
299-300
300-302
306-308
model collections A and B
outline of modeling strategy
overbalancing as a surrogate for
misclassification costs
partitioning the data set
principle component profiles
principle components analysis
CRISP-DM
summary of case study
chapter
298
289-290
302-304
290
292-293
292
265-267
312-315
34, 94
240-241
163, 207
xiii
267
Cereals data set
Chromosomes
Churn data set
Clementine software
Clothing-store data set
Cluster analysis: BIRCH
clustering algorithm
Coefficient of determination
Combining models using the
mean response probabilities
Combining models: voting
Conditional independence
Cook's distance
Correlation coefficient
Cost/benefit table
294-298
39-43
308-312
304-306
216
52
45
267-270
xiii, 265267
240
241
242
242
245
xi
CRISP-DM
Crossover
Crossover operator
Crossover point
Crossover rate
Crowding
Data mining, what is
Datasets
adult
baseball
California
cereals
churn
clothing-store
houses
Deriving new variables
Dimension reduction methods
factor analysis
Bartlett's test of sphericity
equimax rotation
factor analysis model
factor loadings
factor rotation
Kaiser-Meyer-Olkin measure of
18, 176
68
74
34, 94
163, 207
267
5
277-278
1-32
18-23
19
23
18
18, 20
20-23
19
sampling adequacy
oblique rotation
orthogonal rotation
principle axis factoring
quartimax rotation
varimax rotation
multicollinearity
need for
principle components
analysis (PCA)
communalities
component matrix
component weights
components
correlation coefficient
correlation matrix
covariance
covariance matrix
eigenvalue criterion
eigenvalues
eigenvectors
how many components to extract
minimum communality criterion
orthogonality
partial correlation coefficient
principle component
profiling the principle components
proportion of variance explained
criterion
scree plot criterion
standard deviation matrix
validation of the principle
components
summary of dimension
reduction methods chapter
user-defined composites
measurement error
summated scales
Discovering Knowledge in Data, an Introduction to Data
Mining (by Daniel Larose, Wiley, 2005)
Discrete crossover
Elitism
Empirical rule
Estimated regression equation
Estimation error
False negative
False positive
Fitness
23
22
19
23
21
1, 115123
1-2
2-17
15-17
8
8
2
3
4
3
3
10
4
4
9-12
16
9
5
4
13-15
10
11
3
17
25-27
23-25
24
24
xi, 1, 18,
33, 268,
294
249
246
1
35
36
269
269
241
Fitness function
Fitness sharing function
Generation
Genes
Genetic algorithms
241
245
242
240-241
240-264
basic framework of a
genetic algorithm
crossover point
crossover rate
generation
mutation rate
roulette wheel method
241-242
242
242
242
242
242
discrete crossover
normally distributed mutation
simple arithmetic crossover
single arithmetic crossover
whole arithmetic crossover
248-249
249
249
248
248
249
allele
chromosomes
crossover
crossover operator
fitness
fitness function
genes
Holland, John
locus
mutation
mutation operator
population
selection operator
240-241
240-241
240-241
240
241
241
241
240-241
240
240
241
241
241
241
multipoint crossover
positional bias
uniform crossover
247
247
247
247
Boltzmann selection
crowding
elitism
fitness sharing function
rank selection
selection pressure
sigma scaling
245-246
246
245
246
245
246
245
246
genetic algorithms for
real variables
introduction to genetic
algorithms
modifications and
enhancements:
crossover
modifications and
enhancements:
selection
tournament ranking
simple example of a
genetic algorithm at
work
summary of genetic
algorithm chapter
using genetic
algorithms to train a
neural network
243-245
261-262
back-propagation
modified discrete crossover
neural network
WEKA: hands-on
analysis using genetic
algorithms
249-252
250
252
249-250
252-261
49
5
57-63
51
231
36-39
49
158
240
155-203
174-177
High leverage point
Houses data set
Inference in regression
Influential observation
Learning in a Bayesian network
Least-squares estimates
Leverage
Likelihood function
locus
Logistic regression
assumption of linearity
higher order terms to
handle non-linearity
inference: are the
predictors significant?
183-189
deviance
saturated model
Wald test
161-162
160
160
161
for a continuous predictor
for a dichotomous predictor
for a polychotomous predictor
odds
odds ratio
reference cell coding
relative risk
standard error of the coefficients
162-174
170-174
163-166
166-170
162
162
166
163
165-166
interpreting logistic
regression model
interpreting logistic
regression output
maximum likelihood
estimation
159
likelihood function
log likelihood
maximum likelihood estimators
multiple logistic
246
158
158
158
158
179-183
regression
simple example of
conditional mean
logistic regression line
logit transformation
sigmoidal curves
summary of logistic
regression chapter
validating the logistic
regression model
WEKA: hands-on
analysis using logistic
regression
zero-cell problem
156-168
156-157
156-157
158
157
197-199
189-193
194-197
177-179
156-157
158
131
Logistic regression line
Logit transformation
Mallows' Cp statistic
Maximum a posteriori classification
(MAP)
Maximum likelihood estimation
Mean squared error, (MSE)
Minitab software
MIT Technology Review
206-215
158
43
xiii-xiv
xi
1, 115123
179-183
Multicollinearity
Multiple logistic regression
Multiple regression and model
building
93-154
adjusting the
coefficient of
determination
estimated multiple
regression equation
inference in multiple
regression
113
94
100
confidence interval for a particular
coefficient
f-test
t-test
104
102
101
variance inflation factors
96
131
116-123
118-119
interpretation of
coefficients
Mallows' Cp statistic
multicollinearity
multiple coefficient of
determination
multiple regression
model
regression with
categorical predictors
97
99
analysis of variance
dummy variable
105-116
106
106
indicator variable
reference category
sequential sums of
squares
SSE, SSR, SST
summary of multiple
regression and model
building chapter
using the principle
components as
predictors
variable selection
criteria
variable selection
methods
106
107
115
97
147-149
142
135
all possible subsets procedure
application to cereals dataset
backward elimination procedure
best subsets procedure
forward selection procedure
partial f-test
stepwise procedure
Multipoint crossover
Mutation
Mutation operator
Mutation rate
Naïve Bayes classification
Naïve Bayes estimation and Bayesian
networks
Bayesian approach
Bayes, Reverend Thomas
frequentist or classical approach
marginal distribution
maximum a posteriori method
non-informative prior
posterior distribution
prior distribution
Bayesian belief
networks
123-135
126
127-135
125
126
125
123
126
247
241
241
242
215-223
204-239
204-206
205
204
206
206
205
205
205
227-234
conditional independence in Bayesian
networks
directed acyclic graph
joint probability distribution
learning in a Bayesian network
parent node, descendant node
using the Bayesian network to find
probabilities
maximum a posteriori
classification (MAP)
balancing the data
Bayes theorem
227
227
231
231
227
229-232
206-215
212
207
conditional probability
joint conditional probabilities
MAP estimate
posterior odds ratio
naïve Bayes
classification
adjustment for zero frequency cells
conditional independence
log posterior odds ratio
numeric predictors
verifying the conditional
independence assumption
summary of chapter on naïve Bayes estimation and Bayesian
networks
WEKA: hands-on
analysis using naïve
Bayes
WEKA: hands-on
analysis using the
Bayes net classifier
Neural network
Non-informative prior
Normally distributed mutation
Odds
Odds ratio
Outlier
Overbalancing as a surrogate for
misclassification costs
Partitioning the data set
Population
Positional bias
Posterior distribution
Posterior odds ratio
Prediction error
Prinicple components analysis (PCA)
Prior distribution
Rank selection
Regression coefficients
Regression modeling
Regression modeling
207
209
206-207
210-211
215-223
218-219
216
217-218
219-223
218
234-236
223-226
232-234
249-250
205
249
162
162
48
302-304
290
241
247
205
210-211
36
2-17
205
246
35
33-92
46
ANOVA table
coefficient of
determination
correlation coefficient
estimated regression
equation
estimation error
example of simple
linear regression
inference in regression
39-43
45
35
36
34
57-63
confidence interval for the mean value
of y given x
60
confidence interval for the slope
prediction interval
t-test for the relationship between x
and y
least-squares estimates
error term
least-squares line
true or population regression equation
mean squared error,
(MSE)
outliers, high leverage
points, influential
observations
60
61
58
36-39
36
36
36
43
Cook's distance
high leverage point
influential observation
leverage
outlier
standard error of the residual
standardized residual
prediction error
regression coefficients
regression model
assumptions
residual error
slope of the regression
line
standard error of the
estimate, (s)
sum of squares error,
(SSE)
sum of squares
regression, (SSR)
48-55
52
49
51
49
48
48
48
36
35
55-57
55
36
35
43-44
40
41-42
sum of squares total,
(SST)
summary of regression
modeling chapter
transformations to
achieve linearity
41
84-86
Box-Cox transformations
bulging rule
ladder of reexpressions
Scrabble
79-84
83
79, 81
79
79-84
Anderson-Darling test for normality
normal probability plot
patterns in the residual plot
quantile
63-68
65
63
67
64
verifying the
regression
assumptions
y-intercept
Regression with categorical
predictors
Relative risk
Residual error
Roulette wheel method
Scrabble
Selection operator
Selection pressure
Sigma scaling
Simple arithmetic crossover
Single arithmetic crossover
Slope of the regression line
Software
105-116
163
36
242
79-84
241
245
246
248
248
35
Clementine software
Minitab software
SPSS software
WEKA software
SPSS software
Standard error of the estimate, (s)
Steck, James
Tournament ranking
Transformations to achieve linearity
Transformations to achieve normality
or symmetry
True negative
True positive
Uniform crossover
User-defined composites
Variable selection methods
Variance inflation factors
Website, companion
WEKA software
WEKA: Hands-on analysis
White-box approach
Whole arithmetic crossover
www.dataminingconsultant.com
y-intercept
ZDNET news
35
Bayes net classifier
Genetic algorithms
Logistic regression
Naïve Bayes
xiii
xiii-xiv
xiii-xiv
xiii-xiv
xiii-xiv
43-44
xiv
246
79-84
272-275
268
268
247
23-25
123-135
118-119
xii, xiv
xiii-xiv
232-234
252-261
194-197
223-226
xi
249
xii
35
xi