Index

A

accuracy 5.3.1.1, 5.3.2
active sampling 23.1.2
ADP
- See: Automatic Data Preparation
Aggregates
- performance 11.4.7
Algorithm
- Data Preparation 15.3
- ESA 15.1.1, 15.1.2, 15.3
- Explicit Semantic Analysis
  - Data Preparation
    - ESA 15.3
  - Scoring 15.1.1
- Large ESA 15.1.2
- Neural Network 21.4
  - Scoring 21.4
- Random Forest 25.3
  - Scoring 25.3
- Scoring 15.1.1, 21.4, 25.3
  - Large ESA 15.1.2
algorithms 3.2.1, 3.2.2
- Apriori 3.2.2, 8.3, 11, 11.3.4, 11.3.4.1
- association 11.1
- Decision Tree 3.2.1, 13
- defined 3.2
- Expectation Maximization 3.2.2, 14
- Exponential Smoothing 3.2.1, 10.4
- Generalized Linear Models 3.2.1, 17
- k-Means 3.2.2, 7.3, 18
- Minimum Description Length 3.2.1, 19
- Naive Bayes 3.2.1, 20
- Neural Network 3.2.1
- Non-Negative Matrix Factorization 3.2.2, 22
- O-Cluster 3.2.2, 7.3, 23
- One-Class Support Vector Machine 3.2.2, 27.5
- Principal Component Analysis 3.2.2, 26.1
- Random Forest 3.2.1
- Singular Value Decomposition 3.2.2, 26
- supervised 3.2.1
- Support Vector Machines 3.2.1
- unsupervised 3.2.2
Algorithms
- About Algorithm Meta Data Registration 24.3
- About CUR Matrix Decomposition 12.1
- About ESA 15.1
- About Exponential Smoothing 16.1
- About Neural Network 21.1
- About Random Forest 25.1
- Accumulation 16.2.2
- Algorithm Meta Data Registration 24, 24.3
- Building a Random Forest 25.2
- Column selection or attribute selection and row selection 12.4
- CUR matrix decomposition 12, 12.1, 12.2, 12.3, 12.4
- Data preparation 16.2
- double exponential smoothing 16.1.3
- ESA 15.1, 15.2
- Explicit Semantic Analysis 15.1
  - text mining 15.2
- Exponential Smoothing 12.1, 12.2, 15.4, 16, 16.1, 16.1.1, 16.1.2, 16.1.3, 16.1.4, 16.1.5, 16.1.6, 16.2.1, 16.2.2, 16.2.3, 16.2.4, 16.2.5
- Exponential Smoothing Models 16.1.1, 16.2, 16.2.1, 16.2.2, 16.2.3, 16.2.4, 16.2.5
- Input Data 16.2.1
- Missing value 16.2.3
- Neural Network 21, 21.1
  - Convergence Check 21.1.6
  - Forward-backward propagation 21.1.3
  - LBFGS_SCALE_HESSIAN 21.1.7
  - Loss or Cost function 21.1.2
  - Neuron and activation function 21.1.1
  - NNET_HELDASIDE_MAX_FAIL 21.1.8
  - Optimization Solver 21.1.4
  - Regularization 21.1.5
- Parallellish by partition 16.2.4, 16.2.5
- Prediction intervals 16.1.6
- Random Forest 25, 25.1, 25.2
- Seasonality 16.1.4
- Simple Exponential Smoothing 16.1.2
- Singular vectors 12.2
- Statistical Leverage Score 12.3
- Terminologies in Explicit Semantic Analysis 15.4
- Text Mining 15.2
- trend 16.1.3
- Trend and Seasonality 16.1.5
anomaly detection 3.1.2.1, 3.2.2, 5.3.2, 6, 7.1
apply
- See: scoring
Apriori 3.2.2, 8.3, 11
- aggregates 11.4.4
- example: calculating aggregates 11.4.5
- excluding rules 11.4.6
- including rules 11.4.6
- minimum support count 11.5.2
- reverse confidence 11.5.4
artificial intelligence 3.1
association rules 3.1.2.1, 3.2.2, 8, 11
attribute importance 3.1.1.2, 3.2.1, 3.2.2, 9, 19.1
- CUR matrix decomposition 9.4
- 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 20.1
binning 2.6, 22.3
- equi-width 19.2, 23.3
- supervised 19.2, 20.3

C

case table 1.3.2
categorical target 5
centroid 7.1.1, 18.1.2
classification 3.1.1.2, 3.2.1, 5
- biasing 5.3
- binary 5.1, 17.8
- Decision Tree 5.4, 13
- default algorithm 5.4
- Generalized Linear Models 5.4, 17
- logistic regression 17.8
- multiclass 5.1
- Naive Bayes 5.4, 20
- one class 6.1.1
- Support Vector Machines 5.4, 27.4
class weights 5.3.2
clustering 3.1.2.1, 3.2.2, 7
- Expectation Maximization 14
- hierarchical 3.2.2, 7.1.3, 7.2
- K-Means 18.1
- O-Cluster 23
- scoring 3.1.2.1
coefficients
- GLM 17.2.1
- Non-Negative Matrix Factorization 3.2.2, 22.1
- regression 4.1.1, 4.1.1.3
computational learning 1.1.6
confidence 1.1.2
- Apriori 3.2.2, 11.2.2
- association rules 8.1.1, 11.5.3
- clustering 7.1.3.2
- defined 1.1.2
confidence bounds 3.2.1, 4.1.1.6, 17.2.3
confusion matrix 1.3.3, 5.2.1, 5.3.1.1
cost matrix 5.3.1, 13.2.2
costs 1.3.3, 5.3.1
CUR matrix decomposition
- configuration 12.5

D

data mining
- defined 1.1
- Oracle 2, 3
- process 1.3
data preparation 1.2.2, 1.3.2
- for Apriori 11.3
- for Expectation Maximization 14.4
- for Generalized Linear Models 17.6
- for k-Means 18.3
- for Minimum Description Length 19.2
- for Naive Bayes 20.3
- for Neural Network 21.2
- for O-Cluster 23.3
- for SVD 26.3
data warehouse 1.1.7
DBMS_DATA_MINING 2.5.1
DBMS_STAT_FUNCS 2.6
Decision Tree 3.2.1, 13
deployment 1.3.4
descriptive models 3.1.2
directed learning 3.1.1

E

embedded data preparation 1.2.2
entropy 13.2.1, 19.1.1, 19.1.1.3
Exadata 2.3
Expectation Maximization 3.2.2
Explicit Semantic Analysis 3.2.1, 3.2.2
Exponential Smoothing 3.2.1, 10.4

F

feature extraction 3.1.2.1, 3.2.2, 9, 22, 26
- 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 17.3, 17.3.2
feature selection 9, 17.3, 17.3.1
frequent itemsets 11.1, 11.4.2
Functions 24.1

G

Generalized Linear Models 3.2.1, 17
- classification 17.8.1
- feature selection and creation 17.3
- regression 17.7
GLM
- See: Generalized Linear Models
- Solvers 17.5
graphical user interface 2.5.3

H

hierarchies 3.2.2, 7.1.3, 7.2

I

inductive inference 1.1.6
Interior Point Method 27.1.1
itemsets 11.4

K

kernel 2.2
k-Means 3.2.2, 7.3, 18

L

lift 1.3.3
- association rules 11.5.5
- classification 5.2.2
linear regression 3.2.1, 4.1.1.1, 17.7
logistic regression 3.2.1, 17.8

M

machine learning 3.1, 3.2.1
market basket data 1.3.3
market-basket data 8.2
MDL 3.2.1
- See: Minimum Description Length
Minimum Description Length 19
Mining Function
- classification 15.1
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, 3.2.2, 9, 19.1
- 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
- time series 3.2.1
Mining functions
- Time Series
  - Statistics 10.3
Mining Functions
- Build apply 10.3.4
- Conditional Log-Likelihood 10.3.1
- Irregular Time Series 10.3.3
- MSE 10.3.2
- Prediction 10.3.2
- Time Series 10, 10.3.1, 10.3.2, 10.3.3, 10.3.4
missing value treatment 3.3.1
model details 1.3.4, 13.1.1
multicollinearity 17.2.4
multidimensional analysis 1.1.6, 2.6
multivariate linear regression 4.1.1.2

N

Naive Bayes 3.2.1, 20
nested data 11.3.1
Neural Network 3.2.1
- Configuration 21.3
NMF
- See: Non-Negative Matrix Factorization
nonlinear regression 4.1.1.4
Non-Negative Matrix Factorization 3.2.2, 22
nontransactional data 8.2
numerical target 5.1

O

O-Cluster 3.2.2, 7.3, 23
OLAP 1.1.6, 2.6
One-Class Support Vector Machine 3.2.2, 27.5
Oracle Business Intelligence Suite Enterprise Edition 2.6
Oracle Database
- kernel 2.1
- statistical functions 2.6
Oracle Data Miner 2.5.3
Oracle OLAP 2.6
Oracle Spatial 2.6
Oracle Text 2.6
outliers 1.2.2, 6.1.3, 23.3.1
overfitting 3.1.1.1, 13.2.3

P

parallel execution 3.4.1, 11.1, 13.1.2, 19.1, 20.1.1
Partitioned model 2.4
PCA
- See: Principal Component Analysis
PL/SQL API 2.5, 2.5.1
PREDICTION_PROBABILITY function 2.6
PREDICTION Function 2.5.2
predictive analytics 2.5.4
predictive models 3.1.1
Principal Component Analysis 3.2.2, 26.1
prior probabilities 5.3.2, 20.1

R

Random Forest 3.2.1
Receiver Operating Characteristic 5.2.3
Registration 24.1
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, 17
- linear 4.1.1.1, 17.7
- nonlinear 4.1.1.4
- ridge 17.2.4
- statistics 4.2.1
- Support Vector Machine 4.3, 27.6
R extensibility 24.1
R extensible
- Build and score with R 24.2
ridge regression 17.2.4
ROC
- See: Receiver Operating Characteristic
R scripts registration 24.1
rules 1.3.4
- Apriori 11.1
- association rules 8.1.1
- clustering 7.1.3.1
- Decision Tree 13.1.1
- defined 1.1.2

S

sampling 11.3.4
sampling implementation 11.3.4.1
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 2.3
- knowledge deployment 1.3.4
- model details 1.3.4
- Non-Negative Matrix Factorization 22.1.2
- O-Cluster 23.1.4
- 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 17.2.4
Singular Value Decomposition 26
sparse data 3.3.1, 11.3
SQL data mining functions 2.5, 2.5.2
SQL statistical functions 2.6
star schema 11.3.1
statistical functions 2.6
statistics 1.1.5
stratified sampling 5.3.2, 6.1.1
Sub-Gradient Descent 27.1.1
supervised learning 3.1.1
support 1.1.2
- Apriori 3.2.2, 11.4.2
- association rules 8.1.1, 11.5.1
- clustering 7.1.3.2
- defined 1.1.2
Support Vector Machine 3.2.1, 27
- classification 3.2.1, 27.4
- Gaussian kernel 3.2.1
- linear kernel 3.2.1
- one class 3.2.2, 27.5
- regression 3.2.1, 27.6
SVD
- See: Singular Value Decomposition
SVM
- See: Support Vector Machine

T

target 3.1.1, 3.1.2.1
text mining 3.3.3, 22.1.3
time series 3.2.1
transactional data 1.3.3, 8.2, 11.3
transformations 3.3.1
transparency 3.3.1, 7.2, 13.1.1, 17.2.1

U

unstructured data 3.3.3
unsupervised learning 3.1.2
UTL_NLA 2.6

W

wide data 9.1, 17.2.2