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Index

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

A

accuracy 5.3.1.1, 5.3.2
active sampling 19.1.2
ADP
- See: Automatic Data Preparation
Aggregates
- performance 10.4.7
Algorithm
- Data Preparation 13.3
- ESA 13.1.1, 13.1.2, 13.3
- Explicit Semantic Analysis
  - Data Preparation
    - ESA 13.3
  - Scoring 13.1.1
- Large ESA 13.1.2
- Scoring 13.1.1
  - Large ESA 13.1.2
algorithms 3.2.2
- 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, 14
- k-Means 3.2.2, 7.3, 15
- Minimum Description Length 3.2.1, 16
- Naive Bayes 3.2.1, 17
- Non-Negative Matrix Factorization 3.2.2, 18
- O-Cluster 3.2.2, 7.3, 19
- One-Class Support Vector Machine 3.2.2, 21.5
- Principal Component Analysis 3.2.2, 20.1
- Singular Value Decomposition 3.2.2, 20
- supervised 3.2.1
- Support Vector Machines 3.2.1
- unsupervised 3.2.2
Algorithms
- About ESA 13.1
- ESA 13.1, 13.2
- Explicit Semantic Analysis 13.1
  - text mining 13.2
- Text Mining 13.2
anomaly detection 3.1.2.1, 3.2.2, 5.3.2, 6, 7.1
apply
- See: scoring
Apriori 3.2.2, 8.3, 10
- aggregates 10.4.4
- example: calculating aggregates 10.4.5
- excluding rules 10.4.6
- including rules 10.4.6
- minimum support count 10.5.2
- reverse confidence 10.5.4
artificial intelligence 3.1
association rules 3.1.2.1, 3.2.2, 8, 10
attribute importance 3.1.1.2, 3.2.1, 9, 16.1
- Minimum Description Length 9.4
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 17.1
binning 2.7, 18.3
- equi-width 16.2, 19.3
- supervised 16.2, 17.3

C

case table 1.3.2
categorical target 5
centroid 7.1.1, 15.1.2
classification 3.1.1.2, 3.2.1, 5
- biasing 5.3
- binary 5.1, 14.7
- Decision Tree 5.4, 11
- default algorithm 5.4
- Generalized Linear Models 5.4, 14
- logistic regression 14.7
- multiclass 5.1
- Naive Bayes 5.4, 17
- one class 6.1.1
- Support Vector Machines 5.4, 21.4
class weights 5.3.2
clustering 3.1.2.1, 3.2.2, 7
- Expectation Maximization 12
- hierarchical 3.2.2, 7.1.3, 7.2
- K-Means 15.1
- O-Cluster 19
- scoring 3.1.2.1
coefficients
- GLM 14.2.1
- Non-Negative Matrix Factorization 3.2.2, 18.1
- regression 4.1.1, 4.1.1.3
computational learning 1.1.6
confidence 1.1.2
- Apriori 3.2.2, 10.2.2
- association rules 8.1.1, 10.5.3
- clustering 7.1.3.2
- defined 1.1.2
confidence bounds 3.2.1, 4.1.1.6, 14.2.3
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 14.5
- for k-Means 15.3
- for Minimum Description Length 16.2
- for Naive Bayes 17.3
- for O-Cluster 19.3
- for SVD 20.3
data warehouse 1.1.7
DBMS_DATA_MINING 2.6.1
DBMS_STAT_FUNCS 2.7
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, 16.1.1, 16.1.1.3
Exadata 2.4
Expectation Maximization 3.2.2
Explicit Semantic Analysis 3.2.2

F

feature extraction 3.1.2.1, 3.2.2, 9, 18, 20
- default algorithm 9.4
- Explicit Semantic Analysis 9.4
- Non-Negative Matrix Factorization 9.4
- Principal Component Analysis 9.4
- Singular Value Decomposition 9.4
feature generation 14.3, 14.3.2
feature selection 9, 14.3, 14.3.1
frequent itemsets 10.1, 10.4.2
Functions 2.3

G

Generalized Linear Models 3.2.1, 14
- classification 14.7.1
- feature selection and creation 14.3
- regression 14.6
GLM
- See: Generalized Linear Models
graphical user interface 2.6.3

H

hierarchies 3.2.2, 7.1.3, 7.2

I

inductive inference 1.1.6
Interior Point Method 21.1.1
itemsets 10.4

K

kernel 2.2
k-Means 3.2.2, 7.3, 15

L

lift 1.3.3
- association rules 10.5.5
- classification 5.2.2
linear regression 3.2.1, 4.1.1.1, 14.6
logistic regression 3.2.1, 14.7

M

machine learning 3.1
market basket data 1.3.3
market-basket data 8.2
MDL 3.2.1
- See: Minimum Description Length
Minimum Description Length 16
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, 16.1
- 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 14.2.4
multidimensional analysis 1.1.6, 2.7
multivariate linear regression 4.1.1.2

N

Naive Bayes 3.2.1, 17
nested data 10.3.1
NMF
- See: Non-Negative Matrix Factorization
nonlinear regression 4.1.1.4
Non-Negative Matrix Factorization 3.2.2, 18
nontransactional data 8.2
numerical target 5.1

O

O-Cluster 3.2.2, 7.3, 19
OLAP 1.1.6, 2.7
One-Class Support Vector Machine 3.2.2, 21.5
Oracle Business Intelligence Suite Enterprise Edition 2.7
Oracle Database
- kernel 2.1
- statistical functions 2.7
Oracle Data Miner 2.6.3
Oracle OLAP 2.7
Oracle Spatial 2.7
Oracle Text 2.7
outliers 1.2.2, 6.1.3, 19.3.1
overfitting 3.1.1.1, 11.2.3

P

parallel execution 3.4.1, 10.1, 11.1.2, 16.1, 17.1.1
Partitioned model 2.5
PCA
- See: Principal Component Analysis
PL/SQL API 2.6, 2.6.1
PREDICTION_PROBABILITY function 2.7
PREDICTION Function 2.6.2
predictive analytics 2.6.4
predictive models 3.1.1
Principal Component Analysis 3.2.2, 20.1
prior probabilities 5.3.2, 17.1

R

Receiver Operating Characteristic 5.2.3
Registration 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, 14
- linear 4.1.1.1, 14.6
- nonlinear 4.1.1.4
- ridge 14.2.4
- statistics 4.2.1
- Support Vector Machine 4.3, 21.6
R extensibility 2.3
ridge regression 14.2.4
ROC
- See: Receiver Operating Characteristic
R scripts registration 2.3
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
- 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 2.4
- knowledge deployment 1.3.4
- model details 1.3.4
- Non-Negative Matrix Factorization 18.1.2
- O-Cluster 19.1.4
- 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
singularity 14.2.4
Singular Value Decomposition 20
sparse data 3.3.1, 10.3
SQL data mining functions 2.6, 2.6.2
SQL statistical functions 2.7
star schema 10.3.1
statistical functions 2.7
statistics 1.1.5
stratified sampling 5.3.2, 6.1.1
Sub-Gradient Descent 21.1.1
supervised learning 3.1.1
support 1.1.2
- 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, 21
- classification 3.2.1, 21.4
- Gaussian kernel 3.2.1
- linear kernel 3.2.1
- one class 3.2.2, 21.5
- regression 3.2.1, 21.6
SVD
- See: Singular Value Decomposition
SVM
- See: Support Vector Machine

T

target 3.1.1, 3.1.2.1
text mining 3.3.3, 18.1.3
transactional data 1.3.3, 8.2, 10.3
transformations 3.3.1
transparency 3.3.1, 7.2, 11.1.1, 14.2.1

U

unstructured data 3.3.3
unsupervised learning 3.1.2
UTL_NLA 2.7

W

wide data 9.1, 14.2.2