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Oracle® Data Mining Concepts
12c Release 1 (12.1)

E17692-15
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Index

A  B  C  D  E  F  G  H  I  K  L  M  N  O  P  R  S  T  U  V  W 

A

accuracy, 5.3.1.1, 5.3.2
active learning, 20.1.4
active sampling, 18.1.2
ADP
See Automatic Data Preparation
algorithms
Apriori, 3.2.2, 8.3, 10
association, 10.1
Decision Tree, 3.2.1, 11
defined, 3.2
Expectation Maximization, 3.2.2, 12
Generalized Linear Models, 3.2.1, 13
k-Means, 3.2.2, 7.3, 14
Minimum Description Length, 3.2.1, 15
Naive Bayes, 3.2.1, 16
Non-Negative Matrix Factorization, 3.2.2, 17
O-Cluster, 3.2.2, 7.3, 18
One-Class Support Vector Machine, 3.2.2, 20.5
Principal Component Analysis, 3.2.2, 19.1
Singular Value Decomposition, 3.2.2, 19
supervised, 3.2.1, 3.2.1
Support Vector Machines, 3.2.1
unsupervised, 3.2.2
anomaly detection, 3.1.2.1, 3.1.2.1, 3.1.2.1, 3.2.2, 5.3.2, 6, 7.1
apply
See scoring
Apriori, 3.2.2, 8.3, 10
artificial intelligence, 3.1
association rules, 3.1.2.1, 3.1.2.1, 3.2.2, 8, 10
attribute importance, 3.1.1.2, 3.2.1, 9, 15.1
Minimum Description Length, 9.4
See Also feature selection
attributes, 1.3.1, 3.1.2.1
Automatic Data Preparation, 1.2.2, 1.3.2, 1.3.3, 3.3.1

B

Bayes Theorem, 16.1
binning, 2.5, 17.3
equi-width, 14.2, 15.2, 18.3
supervised, 15.2, 16.3

C

case table, 1.3.2
categorical target, 5
centroid, 7.1.1, 14.1.2
class weights, 5.3.2
classification, 3.1.1.2, 3.2.1, 5
biasing, 5.3
binary, 5.1, 13.7
Decision Tree, 5.4, 11
default algorithm, 5.4
Generalized Linear Models, 5.4, 13
logistic regression, 13.7
multiclass, 5.1
Naive Bayes, 5.4, 16
one class, 6.1.1
Support Vector Machines, 5.4, 20.4
clustering, 3.1.2.1, 3.1.2.1, 3.2.2, 7
Expectation Maximization, 12
hierarchical, 3.2.2, 7.1.3, 7.2
K-Means, 14.1
O-Cluster, 18
scoring, 3.1.2.1, 3.1.2.1
coefficients
GLM, 13.2.1
Non-Negative Matrix Factorization, 3.2.2, 17.1
regression, 4.1.1, 4.1.1.3
computational learning, 1.1.6
confidence
Apriori, 3.2.2, 10.2.2
association rules, 8.1.1, 10.5.2
clustering, 7.1.3.2
defined, 1.1.2
confidence bounds, 3.2.1, 4.1.1.6, 13.2.3
confusion matrix, 1.3.3, 5.2.1, 5.3.1.1
cost matrix, 5.3.1, 11.2.2
costs, 1.3.3, 5.3.1

D

data mining
defined, 1.1
Oracle, 2, 3
process, 1.3
data preparation, 1.2.2, 1.3.2
for Apriori, 10.3
for Expectation Maximization, 12.4
for Generalized Linear Models, 13.5
for k-Means, 14.3
for Minimum Description Length, 15.2
for Naive Bayes, 16.3
for O-Cluster, 18.3
for SVD, 19.3
data warehouse, 1.1.7
DBMS_DATA_MINING, 2.4.1
DBMS_DATA_MINING_TRANSFORM, 2.5, 3.3.1
DBMS_PREDICTIVE_ANALYTICS, 2.4.4
DBMS_STAT_FUNCS, 2.5
Decision Tree, 3.2.1, 11
deployment, 1.3.4
descriptive models, 3.1.2
directed learning, 3.1.1

E

embedded data preparation, 1.2.2
entropy, 11.2.1, 15.1.1, 15.1.1.3
Exadata, 2.3
Expectation Maximization, 3.2.2

F

feature creation, Preface
feature extraction, 3.1.2.1, 3.1.2.1, 3.2.2, 9, 17, 19, 19
default algorithm, 9.4
Non-Negative Matrix Factorization, 9.4
Principal Component Analysis, 9.4
Singular Value Decomposition, 9.4
feature generation, 13.3, 13.3.2
feature selection, Preface, 9, 13.3, 13.3.1
See Also attribute importance
frequent itemsets, 10.1, 10.4.2

G

Generalized Linear Models, 3.2.1, 13
classification, 13.7.1
feature selection and creation, Preface, 13.3
regression, 13.6
GLM
See Generalized Linear Models
graphical user interface, 2.4.3

H

hierarchies, 3.2.2, 7.1.3, 7.2
histogram, 14.2

I

inductive inference, 1.1.6
itemsets, 10.4

K

kernel, 2.2
k-Means, 3.2.2, 7.3, 14

L

lift, 1.3.3
association rules, 10.5.3
classification, 5.2.2
linear regression, 3.2.1, 4.1.1.1, 13.6
logistic regression, 3.2.1, 13.7

M

machine learning, 3.1
market basket data, 1.3.3
MDL, 3.2.1
See Minimum Description Length
Minimum Description Length, 15
mining functions, 3.1, 3.1.1.2
anomaly detection, 3.1.2.1, 3.2.2, 6
association rules, 3.1.2.1, 3.2.2, 8
attribute importance, 3.2.1, 9, 15.1
classification, 3.1.1.2, 3.2.1, 5
clustering, 3.1.2.1, 3.2.2, 7
feature extraction, 3.1.2.1, 3.2.2, 9, 9.3
regression, 3.1.1.2, 3.2.1, 4
missing value treatment, 3.3.1
model details, 1.3.4, 11.1.1
multicollinearity, 13.2.4
multidimensional analysis, 1.1.6, 2.5, 2.5
multivariate linear regression, 4.1.1.2

N

Naive Bayes, 3.2.1, 16
nested data, 10.3.1
neural networks, 20.1.1
NMF
See Non-Negative Matrix Factorization
nonlinear regression, 4.1.1.4
Non-Negative Matrix Factorization, 3.2.2, 17
nontransactional data, 8.2
numerical target, 5.1

O

O-Cluster, 3.2.2, 7.3, 18
OLAP, 1.1.6, 2.5
One-Class Support Vector Machine, 3.2.2, 20.5
Oracle Business Intelligence Suite Enterprise Edition, 2.5
Oracle Data Miner, 2.4.3
Oracle Database
kernel, 2.1
statistical functions, 2.5
Oracle OLAP, 2.5
Oracle Spatial, 2.5
Oracle Text, 2.5, 2.5
outliers, 1.2.2, 6.1.3, 18.3.1
overfitting, 3.1.1.1, 11.2.3

P

parallel execution, 3.4.1, 10.1, 11.1.2, 12.2.1, 15.1, 16.1.1
PCA
See Principal Component Analysis
PL/SQL API, 2.4, 2.4.1
PREDICTION Function, 2.4.2
PREDICTION_PROBABILITY function, 2.5
predictive analytics, 2.4.4
predictive models, 3.1.1
Principal Component Analysis, 3.2.2, 19.1
See Also Singular Value Decomposition
prior probabilities, 5.3.2, 16.1

R

radial basis functions, 20.1.1
Receiver Operating Characteristic, 5.2.3
regression, 3.1.1.2, 3.2.1, 4
coefficients, 4.1.1, 4.1.1.3
default algorithm, 4.3
defined, 4.1.1
Generalized Linear Models, 4.3, 13
linear, 4.1.1.1, 13.6
nonlinear, 4.1.1.4
ridge, 13.2.4
statistics, 4.2.1
Support Vector Machine, 4.3, 20.6
ridge regression, 13.2.4
ROC
See Receiver Operating Characteristic
rules, 1.3.4
Apriori, 10.1
association rules, 8.1.1
clustering, 7.1.3.1
Decision Tree, 11.1.1
defined, 1.1.2

S

scoring, 3.1.2.1, 3.1.2.1, 3.1.2.1
anomaly detection, 3.1.2.1
classification, 3.1.1.2
clustering, 3.1.2.1
defined, 1.1.1
dynamic, 3.4.2
Exadata, 2.3
knowledge deployment, 1.3.4
model details, 1.3.4
Non-Negative Matrix Factorization, 17.1.2
O-Cluster, 18.1.4
parallel execution, 3.4.1
real time, 1.3.4
regression, 3.1.1.1
supervised models, 3.1.1.2
unsupervised models, 3.1.2.1
Singular Value Decomposition, 19
See Also Principal Component Analysis
singularity, 13.2.4
sparse data, 3.3.1, 10.3
SQL data mining functions, 2.4, 2.4.2
SQL statistical functions, 2.5
star schema, 10.3.1
statistical functions, 2.5
statistics, 1.1.5
stratified sampling, 5.3.2, 6.1.1
supervised learning, 3.1.1
support
Apriori, 3.2.2, 10.4.2
association rules, 8.1.1, 10.5.1
clustering, 7.1.3.2
defined, 1.1.2
Support Vector Machine, 3.2.1, 20
classification, 3.2.1, 20.4
Gaussian kernel, 3.2.1
linear kernel, 3.2.1
one class, 3.2.2, 20.5
regression, 3.2.1, 20.6
SVD
See Singular Value Decomposition
SVM
See Support Vector Machine

T

target, 3.1.1, 3.1.2.1
text mining, 3.3.3, 17.1.3
transactional data, 1.3.3, 8.2, 10.3
transformations, 3.3.1
transparency, 3.3.1, 7.2, 11.1.1, 13.2.1

U

unstructured data, 3.3.3
unsupervised learning, 3.1.2
UTL_NLA, 2.5

V

Variance Inflation Factor, 13.2.4.3

W

wide data, 9.1, 13.2.2