Index

A

ADP 4.5.2
ALGO_EXTENSIBLE_LANG 4.8.6.1
algorithms 4.1, 4.3
- metadata registration 4.8.6.8
- parallel execution 8.1.2
- used by examples A.1
ALL_MINING_MODEL_ATTRIBUTES 2.2
ALL_MINING_MODEL_PARTITIONS 2.2
ALL_MINING_MODEL_SETTINGS 2.2, 4.8.5
ALL_MINING_MODEL_VIEWS 2.2
ALL_MINING_MODEL_XFORMS 2.2
ALL_MINING_MODELS 2.2
anomaly detection 2.1, 3.1.4, 4.2, 4.3, 5.7
APPLY 5.1
APPROX_COUNT 2.5
APPROX_RANK 2.5
APPROX_SUM 2.5
Apriori 3.4, 4.2, 4.3, 4.4.3
- example: calculating aggregates 3.5.1
association rules 4.2, 4.3
- model detail view 4.9.1
attribute importance 2.1, 4.2, 4.3
attributes 3.1.1, 3.2, 6.3
- categorical 3.2.3, 6.1
- data attributes 3.2.1
- data dictionary 2.2
- model attributes 3.2.1, 3.2.3
- nested 3.1.1
- numerical 3.2.3, 6.1
- subname 3.2.5
- target 3.2.2
- text 3.2.3
- unstructured text 6.2
attribute specification 4.5.3, 6.5
AUDIT 8.4.2, 8.5.2
Automatic Data Preparation 1.1, 3.1.4, 4.4

B

binning 4.4.1
- equi-width 4.5.4.1
- quantile 4.5.4.1
- supervised 4.4.1, 4.5.4.1
- top-n frequency 4.5.4.1
build data 3.1.3

C

case ID 3.1, 3.1.1, 3.2.4, 5.7
case table 3.1, 3.8
categorical attributes 6.1
classification 2.1, 3.1.3, 3.1.4, 3.2.2, 4.2, 4.3
class weights 4.8.3
clipping 4.5.4.3
CLUSTER_DETAILS 1.4, 2.4
CLUSTER_DISTANCE 2.4
CLUSTER_ID 1.4, 2.4
CLUSTER_PROBABILITY 2.4
CLUSTER_SET 1.4, 2.4
clustering 1.4, 2.1, 3.1.4, 4.3
COMMENT 8.4.2
CORR 2.5
CORR_K 2.5
CORR_S 2.5
cost matrix 4.8.1, 5.6, 8.4.3
cost-sensitive prediction 5.6
COVAR_POP 2.5
COVAR_SAMP 2.5
CUR Matrix Decomposition 4.2, 4.3, 4.4.3

D

data
- categorical 3.2.3
- dimensioned 3.3.2
- for examples A.3
- market basket 3.4
- missing values 3.6
- multi-record case 3.3.2
- nested 3.1.1
- numerical 3.2.3
- READ access 8.4.1.1
- SELECT access 8.4.1.1
- single-record case 3.1
- sparse 3.6
- transactional 3.4
- unstructured text 3.2.3
Database Upgrade Assistant 8.2.2.1
Data preparation
- model view
  - text features 4.9.27
data types 3.1.1, 3.8.1
- nested 3.3
DBMS_DATA_MINING 2.3, 2.3.1, 4.2
DBMS_DATA_MINING_TRANSFORM 2.3, 2.3.2
DBMS_PREDICTIVE_ANALYTICS 1.3, 2.3, 2.3.3
Decision Tree 4.2, 4.3, 4.4.3, 5.4
directory objects 8.3.4
DM$VA 4.9.8, 4.9.11, 4.9.12, 4.9.16, 4.9.18, 4.9.22
DM$VB 4.9.10, 4.9.16, 4.9.18, 4.9.22
DM$VC 4.9.7, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14
DM$VD 4.9.8, 4.9.16, 4.9.18
DM$VE 4.9.20
DM$VF 4.9.16
DM$VG 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22, 4.9.26
DM$VH 4.9.16, 4.9.18
DM$VI 4.9.7, 4.9.14, 4.9.16, 4.9.20
DM$VM 4.9.7, 4.9.16
DM$VN 4.9.8, 4.9.9, 4.9.11, 4.9.16, 4.9.20
DM$VO 4.9.7, 4.9.16
DM$VP 4.9.7, 4.9.10, 4.9.16, 4.9.26
DM$VR 4.9.16, 4.9.18, 4.9.26
DM$VS 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22
DM$VT 4.9.7, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.26
DM$VV 4.9.10
DM$VW 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22
downgrading 8.2.4
DROP_ONNX_MODEL 7.6

E

examples A
- data used by A.3
- file names of A.1
- installing A.2
- Oracle Database Examples A.2
- requirements A.2
- sample schemas for A.2
Expectation Maximization 4.4.3
EXPLAIN 2.3.3
Explicit Semantic Analysis 4.2, 4.3
Exponential Smoothing 4.2, 4.3
Export and Import
- serialized models 8.3.6
exporting 8.2.2.2, 8.2.2.2.1, 8.3

F

FEATURE_COMPARE 2.4
- ESA 1.5
FEATURE_DETAILS 2.4
FEATURE_ID 2.4
FEATURE_SET 2.4
FEATURE_VALUE 2.4
feature extraction 2.1, 3.1.4, 4.2, 4.3

G

Generalized Linear Model 4.4.3
GLM 4.3
graphical user interface 1.1

I

IMPORT_ONNX_MODEL 7.6
importing 8.2.2.2, 8.2.2.2.1, 8.3
installation
- Oracle AI Database 8.1.1
installing
- OML4SQL examples A.2
- Oracle Database A.2
- Oracle Database Examples A.2
- sample schemas A.2

K

k-Means 4.2, 4.3, 4.4.3

L

LAG 2.5
LEAD 2.5
linear regression 2.4, 4.2
LOAD_ONNX_MODEL 7.6
logistic regression 2.4, 4.2

M

machine learning
- database tuning for 8.1.2
- examples A
- privileges for 8.1.1, 8.4.1
- scoring 4.2, 5
machine learning for SQL
- privileges for A.2
machine learning for SQL models
- adding a comment 8.5, 8.5.1
- auditing 8.5.2
- object privileges 8.4.3
machine learning functions 4.1, 4.2
- supervised 4.2
- unsupervised 4.2
- used by examples A.1
machine learning models
- auditing 8.5.2
machine learning models for SQL
- adding a comment 2.1
- applying 8.4.3
- auditing 2.1
- changing the name 8.4.3
- data dictionary 2.2
- privileges for 2.1
- upgrading 8.2.2
- viewing model details 8.4.3
machine learning techniques 2.1
market basket data 3.4
MDL 4.4.3
memory 8.1.2
Minimum Description Length 4.3, 4.4.3
missing value treatment 3.6.2
model attributes
- categorical 3.2.3
- derived from nested column 3.2.5
- numerical 3.2.3
- scoping of name 3.2.5
- text 3.2.3
model details 3.2.6
model detail views 4.9
- association rules 4.9.1
- clustering algorithms 4.9.15
- CUR Matrix Decomposition 4.9.6
- Decision Tree 4.9.7
- EM 4.9.16
- ESM 4.9.26
- Explicit Semantic Analysis 4.9.19
- Exponential Smoothing 4.9.26
- for binning 4.9.23
- for classification algorithms 4.9.5
- for frequent itemsets 4.9.2
- for global information 4.9.24
- for normalization and missing value handling 4.9.25
- for transactional itemsets 4.9.3
- for transactional rules and itemsets 4.9.4
- GLM 4.9.8
- k-Means 4.9.17
- Minimum Description Length 4.9.22
- MSET-SPRT 4.9.9
- Naive Bayes 4.9.10
- Neural Network 4.9.11
- Non-Negative Matrix Factorization 4.9.20
- O-Cluster 4.9.18
- Random Forest 4.9.12
- SVD 4.9.21
- SVM 4.9.13
- XGBoost 4.9.14
model detail views for Random Forest 4.9.12
models
- algorithms 4.3
- deploying 5.1
- partitions 2.2
- privileges for 8.4.1.1
- settings 2.2, 4.8.5
- testing 3.1.3
- training 3.1.3
- transparency 1.1
- XFORMS 2.2
model signature 3.2.4
MSET-SPRT 4.3
Multivariate State Estimation Technique - Sequential Probability Ratio Test 4.2, 4.4.3

N

Naive Bayes 4.2, 4.3, 4.4.3
nested data 3.3, 6.3
Neural Network 4.2, 4.3, 4.4.3
NMF 4.3
non-negative matrix factorization 4.4.3
Non-Negative Matrix Factorization 4.2
normalization 4.4.2
- min-max 4.5.4.2
- scale 4.5.4.2
- z-score 4.5.4.2
numerical attributes 6.1

O

object privileges 8.4.3
O-Cluster 3.3, 4.2, 4.3, 4.4.3
OML4SQL
- applications of 1.1
- example A
One-Class SVM 4.2
ORA_DM_PARTITION_NAME ORA 2.4
Oracle Data Miner 1.1, 8.2.1
Oracle Data Pump 8.3
Oracle machine learning APIs 2.3.1
Oracle Machine Learning for SQL functions 2.4, 2.5
Oracle Text 6.2
outliers 4.5.4.3, 4.5.4.4

P

parallel execution 5.1, 8.1.2
partitioned model 4.8.4
- add partition 4.8.4.2.1
- build 4.8.4.1
- DDL implementation 4.8.4.2
- drop model 4.8.4.2.2, 4.8.4.2.4
- drop partition 4.8.4.2.2, 4.8.4.2.4
- replace partition 4.8.4.2.3
- scoring 4.8.4.3
partitions
- data dictionary 2.2
PGA 8.1.2
PL/SQL packages 2.3
PMML 8.3.7
PREDICTION 1.2, 1.3, 2.4, 5.5
PREDICTION_BOUNDS 2.4
PREDICTION_COST 2.4
PREDICTION_DETAILS 2.4, 5.5
PREDICTION_PROBABILITY 1.3, 2.4, 5.4
PREDICTION_SET 2.4
PREDICTION function
- GROUPING hint 5.3.4
predictive analytics 1.1, 1.3, 2.1, 2.3.3
preparing data
- using retail analysis data aggregates 3.5
prior probabilities 4.8.2
priors table 4.8.2
privileges 8.4.1
- for creating machine learning models 8.2.3
- for machine learning 8.1.1
- for OML4SQL examples A.2
- required for machine learning 8.4.1.1

R

RALG_BUILD_FUNCTION 4.8.6.2
RALG_BUILD_PARAMETER 4.8.6.2.1
RALG_DETAILS_FORMAT 4.8.6.4
RALG_DETAILS_FUNCTION 4.8.6.3
RALG_SCORE_FUNCTION 4.8.6.5
RALG_WEIGHT_FUNCTION 4.8.6.6
Random Forest 4.2, 4.3, 4.4.3, 4.9.12
REGISTER_ALGORITHM procedure 4.8.6.8
regression 2.1, 3.1.3, 3.1.4, 3.2.2, 4.2, 4.3
reverse transformations 3.2.6
R extensible language 4.3
R machine learning model
- settings 4.8.6

S

scoring 1.1, 2.1, 5, 8.1.2, 8.4.3
- data 3.1.3
- dynamic 1.3, 2.1, 5.5
- parallel execution 5.1
- privileges for 8.4.2
- requirements 3.1.4
- SQL functions 2.4, 2.5
- transparency 1.1
settings
- data dictionary 2.2
- table for specifying 4.1
SGA 8.1.2
Singular Value Decomposition 4.4.3
sparse data 3.6
SQL AUDIT 2.1, 8.5.2
SQL COMMENT 2.1, 8.5.1
SQL Developer 1.1
SQL scoring function 2.4
STACK 2.3.2.1, 4.5.1.2
Static Dictionary Views
- ALL_MINING_MODEL_VIEWS 2.2.5
STATS_BINOMIAL_TEST 2.5
STATS_CROSSTAB 2.5
STATS_F_TEST 2.5
STATS_KS_TEST 2.5
STATS_MODE 2.5
STATS_MW_TEST 2.5
STATS_ONE_WAY_ANOVA 2.5
STATS_T_TEST_* 2.5
STATS_T_TEST_INDEP 2.5
STATS_T_TEST_INDEPU 2.5
STATS_T_TEST_ONE 2.5
STATS_T_TEST_PAIRED 2.5
STATS_WSR_TEST 2.5
STDDEV 2.5
STDDEV_POP 2.5
STDDEV_SAMP 2.5
SUM 2.5
Support Vector Machine 4.2, 4.3, 4.4.3
SVD 4.3
system privileges 8.4.2, A.2

T

target 3.2.2, 3.2.3, 3.2.4, 6.3
test data 3.1.3, 4.1
text
- operations on 2.3.2.1, 6
text attributes 6.3, 6.5
text policy 6.4
text terms 6.2
time series 4.2, 4.3
training data 4.1
transactional data 3.1, 3.3.2, 3.4
transformations 2.3.2, 3.1.4, 3.2.1, 3.2.6, 4.1
- attribute-specific 2.3.2, 2.3.2.1
- embedded 2.3.2, 2.3.2.1, 3.1.4
- user-specified 3.1.4
transparency 3.2.6
trimming 4.5.4.4

U

upgrading 8.2.2
- exporting and importing 8.2.2.2, 8.2.2.2.1
- pre-upgrade steps 8.2.1
- using Database Upgrade Assistant 8.2.2.1
users 8.1.1, A.2
- assigning machine learning privileges to 8.4.1.1
- creating 8.4.1
- privileges for machine learning 8.4.1
- privileges for machine learning for SQL 8.2.3

V

VECTOR_EMBEDDING 2.4

W

weights 4.8.3
what are the machine learning SQL API packages 2.3.1
what are the machine learning SQL APIs 2.3.2, 2.3.3
windsorize 4.5.4.4

X

XFORM 2.3.2.1
XFORMS
- data dictionary 2.2
XGBoost 4.2, 4.3
- model detail views 4.9.14
XG Boost 4.4.3