Index

A

accuracy 5.3.1.1, 5.3.2
active sampling 27.1.2
Aggregates
- performance 14.4.7
Algorithm
- ESA 18.2, 18.3, 18.3.1
- Explicit Semantic Analysis
  - Data Preparation
    - ESA 18.2
  - Scoring 18.3
- Large ESA 18.3.1
- Neural Network 25.4
  - Scoring 25.4
- Random Forest 29.3
  - scoring 29.3
- Scoring 18.3, 25.4, 29.3
  - Large ESA 18.3.1
algorithms 3.2.1, 3.2.2
- Apriori 3.2.2, 9.3, 14, 14.3.4, 14.3.4.1
- association 14.1
- Decision Tree 3.2.1, 16
- Expectation Maximization 3.2.2, 17
- Exponential Smoothing 3.2.1, 13.4
- Generalized Linear Model 3.2.1, 20
- k-Means 3.2.2, 8.3, 21
- Minimum Description Length 3.2.1, 22
- Naive Bayes 3.2.1, 24
- Neural Network 3.2.1
  - regularization 25.1.5
- Non-Negative Matrix Factorization 3.2.2, 26
- O-Cluster 3.2.2, 8.3, 27
- One-Class Support Vector Machine 3.2.2, 31.5
- Principal Component Analysis 3.2.2, 30.1
- Random Forest 3.2.1
- Singular Value Decomposition 3.2.2, 30
- supervised 3.2.1
- Support Vector Machine 3.2.1
- unsupervised 3.2.2
- XGBoost 32.1
Algorithms
- About ESA 18.1
- About Exponential Smoothing 19.1
- About Neural Network 25.1
- About Random Forest 29.1
- Accumulation 19.2.2
- algorithm metadata registration 28.2
- Algorithm Meta Data Registration 28
- Building a Random Forest 29.2
- Column selection or attribute selection and row selection 15.4
- CUR matrix decomposition 7.2, 15, 15.3
- CUR Matrix Decomposition 15.1, 15.2, 15.4
- Data preparation 19.2
- double exponential smoothing 19.1.3
- ESA 18.1, 18.1.1
- Explicit Semantic Analysis 18.1, 18.4
  - text mining 18.1.1
- exponential smoothing 19.2.4
- Exponential Smoothing 15.1, 15.2, 19, 19.1, 19.1.1, 19.1.2, 19.1.3, 19.1.4, 19.1.5, 19.1.6, 19.2, 19.2.1, 19.2.2, 19.2.3, 19.2.5
- exponential smoothing models 19.2.4
- Exponential Smoothing models 19.2.2
- Exponential Smoothing Models 19.1.1, 19.2.1, 19.2.3, 19.2.5
- Input Data 19.2.1
- Missing value 19.2.3
- Neural Network 25, 25.1
  - Convergence Check 25.1.6
  - Forward-backward propagation 25.1.3
  - LBFGS_SCALE_HESSIAN 25.1.7
  - Loss or Cost function 25.1.2
  - Neuron and activation function 25.1.1
  - NNET_HELDASIDE_MAX_FAIL 25.1.8
  - optimization solvers 25.1.4
- Parallellish by partition 19.2.4, 19.2.5
- Prediction intervals 19.1.6
- Random Forest 29, 29.1, 29.2
- Seasonality 19.1.4
- Simple Exponential Smoothing 19.1.2
- singular vectors 15.2
- Statistical Leverage Score 7.2, 15.3
- Terminologies in Explicit Semantic Analysis 18.4
- text analysis 18.1.1
- trend 19.1.3
- Trend and Seasonality 19.1.5
anomaly detection 3.1.2.1, 3.2.2, 5.3.2, 6, 6.1, 8.1
- MSET-SPRT 23.1
apply
- See: scoring
Apriori 3.2.2, 9.3, 14
- aggregates 14.4.4
- example: calculating aggregates 14.4.5
- excluding rules 14.4.6
- including rules 14.4.6
- minimum support count 14.5.2
- reverse confidence 14.5.4
artificial intelligence 3.1
association rules 3.1.2.1, 3.2.2, 9, 14
attribute importance 3.1.1.3, 3.2.1, 3.2.2, 10, 22.1
- CUR Matrix Decomposition 10.3
- Minimum Description Length 10.3
attributes 3.1.2.1
Automatic Data Preparation 1.2.2, 3.3.1

B

Bayes Theorem 24.1
binning 2.6, 26.3
- equi-width 22.2, 27.3
- supervised 22.2, 24.3

C

categorical target 5
centroid 8.1.1, 21.1.2
classification 3.1.1.3, 3.2.1, 5
- biasing 5.3
- binary 5.1, 20.8
- Decision Tree 5.4, 16
- default algorithm 5.4
- Explicit Semantic Analysis 5.4
- Generalized Linear Model 5.4, 20
- logistic regression 20.8
- multiclass 5.1
- Naive Bayes 5.4, 24
- one class 6.1.1
- Random Forest 5.4
- Support Vector Machine 5.4, 31.4
- XGBoost 5.4
class weights 5.3.2
clustering 3.1.2.1, 3.2.2, 8
- Expectation Maximization 17
- hierarchical 3.2.2, 8.1.3, 8.2
- k-Means 21.1
- O-Cluster 27
- scoring 3.1.2.1
coefficients
- GLM 20.2.1
- Non-Negative Matrix Factorization 3.2.2, 26.1
- regression 4.1.1, 4.1.1.3
computational learning 1.1.6
confidence 1.1.2
- Apriori 3.2.2, 14.2.2
- association rules 9.1.1, 14.5.3
- clustering 8.1.3.2
- defined 1.1.2
confidence bounds 3.2.1, 4.1.1.6, 20.2.3
confusion matrix 5.2.1, 5.3.1.1
cost matrix 5.3.1, 16.2.2
costs 5.3.1
CUR Matrix Decomposition
- configuration 15.5

D

data preparation 1.2.2
- for Apriori 14.3
- for Expectation Maximization 17.4
- for Generalized Linear Model 20.6
- for k-Means 21.3
- for Minimum Description Length 22.2
- for Naive Bayes 24.3
- for Neural Network 25.2
- for O-Cluster 27.3
- for SVD 30.3
data warehouse 1.1.7
DBMS_DATA_MINING 2.5.1
DBMS_STAT_FUNCS 2.6
Decision Tree 3.2.1, 16
descriptive models 3.1.2
directed learning 3.1.1

E

embedded data preparation 1.2.2
entropy 16.2.1, 22.1.1, 22.1.1.3
Exadata 2.3
Expectation Maximization 3.2.2
Explicit Semantic Analysis 3.2.1, 3.2.2
Exponential Smoothing 3.2.1, 13.4

F

feature extraction 3.1.2.1, 3.2.2, 11, 26, 30
- default algorithm 11.2
- Explicit Semantic Analysis 11.2
- Non-Negative Matrix Factorization 11.2
- Principal Component Analysis 11.2
- Singular Value Decomposition 11.2
feature generation 20.3.2
feature selection 10, 20.3.1
frequent itemsets 14.1, 14.4.2
Functions 28.1

G

Generalized Linear Model 3.2.1, 20
- classification 20.8.1
- feature generation 20.3
- feature selection and creation
  - feature selection 20.3
- regression 20.7
GLM
- See: Generalized Linear Model
- Solvers 20.5
gradient boosting 32.1
graphical user interface 2.5.3

H

hierarchies 3.2.2, 8.1.3, 8.2

I

inductive inference 1.1.6
Interior Point Method 31.1.1
itemsets 14.4

K

kernel 2.2
k-Means 3.2.2, 8.3, 21

L

lift
- association rules 14.5.5
- classification 5.2.2
linear regression 3.2.1, 4.1.1.1, 20.7
logistic regression 3.2.1, 20.8

M

machine learning 3.1, 3.2.1
- Oracle 2, 3
- process 1.3
machine learning function
- anomaly detection 6.1
Machine Learning Function
- classification 18.1
machine learning functions 3.1, 3.1.1.3
- anomaly detection 3.1.2.1, 3.2.2
- association rules 3.1.2.1, 3.2.2, 9
- attribute importance 3.2.1, 3.2.2, 10, 22.1
- Build apply 13.3.4
- classification 3.1.1.3, 3.2.1, 5
- clustering 3.1.2.1, 3.2.2, 8
- feature extraction 3.1.2.1, 3.2.2, 11
- regression 3.1.1.3, 3.2.1, 4
- time series 3.2.1, 13
- Time Series 13.3.4
market-basket data 9.2
MDL 3.2.1
- See: Minimum Description Length
Minimum Description Length 22
mining functions 10
- anomaly detection 6
- feature extraction 11.1
Mining functions
- Time Series
  - Statistics 13.3
Mining Functions
- Conditional Log-Likelihood 13.3.1
- Irregular Time Series 13.3.3
- MSE 13.3.2
- Prediction 13.3.2
- Time Series 13.3.1, 13.3.2, 13.3.3
missing value treatment 3.3.1
model details 16.1.1
MSET-SPRT
- about 23.1
- scoring 23.2
multicollinearity 20.2.4
multidimensional analysis 1.1.6, 2.6
multivariate linear regression 4.1.1.2
Multivariate State Estimation Technique - Sequential Probability Ratio Test 23

N

Naive Bayes 3.2.1, 24
nested data 14.3.1
Neural Network 3.2.1
- configuration 25.3
NMF
- See: Non-Negative Matrix Factorization
nonlinear regression 4.1.1.4
Non-Negative Matrix Factorization 3.2.2, 26
nontransactional data 9.2
numerical target 5.1

O

O-Cluster 3.2.2, 8.3, 27
OLAP 1.1.6, 2.6
OML4SQL
One-Class Support Vector Machine 3.2.2, 31.5
optimization solvers 25.1.4
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, 27.3.1
overfitting 3.1.1.2, 16.2.3

P

parallel execution 3.4.1, 14.1, 16.1.2, 22.1, 24.1.1
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, 30.1
prior probabilities 5.3.2, 24.1

R

Random Forest 3.2.1
Receiver Operating Characteristic 5.2.3
Registration 28.1
regression 3.1.1.3, 3.2.1, 4
- coefficients 4.1.1, 4.1.1.3
- default algorithm 4.3
- defined 4.1.1
- Generalized Linear Model 4.3, 20
- linear 4.1.1.1, 20.7
- Neural Network 4.3
- nonlinear 4.1.1.4
- ridge 20.2.4
- statistics 4.2.1
- Support Vector Machine 4.3, 31.6
- XGBoost 4.3
Regression
- Ranking 7.1
R extensibility 28.1
R extensible
- build and score with R 28.3
ridge regression 20.2.4
ROC
- See: Receiver Operating Characteristic
Row importance 12.1
Row importance algorithm
- CUR Matrix Decomposition 12.2
R scripts registration 28.1
rules
- Apriori 14.1
- association rules 9.1.1
- clustering 8.1.3.1
- Decision Tree 16.1.1
- defined 1.1.2

S

sampling 14.3.4
sampling implementation 14.3.4.1
scoring 3.1.2.1
- anomaly detection 3.1.2.1
- classification 3.1.1.3
- clustering 3.1.2.1
- defined 1.1.1
- dynamic 3.4.2
- Exadata 2.3
- MSET-SPRT 23.2
- Non-Negative Matrix Factorization 26.1.2
- O-Cluster 27.1.4
- parallel execution 3.4.1
- regression 3.1.1.1
- supervised models 3.1.1.3
- unsupervised models 3.1.2.1
- XGBoost 32.2
singularity 20.2.4
Singular Value Decomposition 30
sparse data 3.3.1, 14.3
SQL machine learning functions 2.5, 2.5.2
SQL statistical functions 2.6
star schema 14.3.1
statistical functions 2.6
statistics 1.1.5
stratified sampling 5.3.2, 6.1.1
Sub-Gradient Descent 31.1.1
supervised learning 3.1.1
support 1.1.2
- Apriori 3.2.2, 14.4.2
- association rules 9.1.1, 14.5.1
- clustering 8.1.3.2
- defined 1.1.2
Support Vector Machine 3.2.1, 31
- classification 3.2.1, 31.4
- Gaussian kernel 3.2.1
- linear kernel 3.2.1
- one class 3.2.2, 31.5
- regression 3.2.1, 31.6
SVD
- See: Singular Value Decomposition
SVM
- See: Support Vector Machine

T

target 3.1.1, 3.1.2.1
text analysis 26.1.3
text mining 3.3.3
time series 3.2.1
transactional data 9.2, 14.3
transformations 3.3.1
transparency 3.3.1, 8.2, 16.1.1, 20.2.1

U

unstructured data 3.3.3
unsupervised learning 3.1.2
UTL_NLA 2.6

W

wide data 10.1, 20.2.2

X

XGBoost 7.3
- scoring 32.2
XGBoost algorithm
- about 32.1