Index

A

ADP 4.5.2
ALGO_EXTENSIBLE_LANG 4.8.5.1
algorithms 4.1, 4.3
- metadata registration 4.8.5.8
- parallel execution 7.1.2
- used by examples A.1
ALL_MINING_MODEL_ATTRIBUTES 2.2
ALL_MINING_MODEL_PARTITIONS 2.2
ALL_MINING_MODEL_SETTINGS 2.2, 4.9
ALL_MINING_MODEL_VIEWS 2.2
ALL_MINING_MODEL_XFORMS 2.2
ALL_MINING_MODELS 2.2
anomaly detection 2.1, 3.1.3, 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 4.10.1
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 7.4.2, 7.5.2
Automatic Data Preparation 1.1, 3.1.3, 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 3.1.2

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, 3.1.2, 3.1.3, 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, 3.1.3, 4.3
COMMENT 7.4.2
CORR 2.5
CORR_K 2.5
CORR_S 2.5
cost matrix 4.8.1, 5.6, 7.4.3
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 7.4.1.1
- SELECT access 7.4.1.1
- single-record case 3.1
- sparse 3.6
- transactional 3.4
- unstructured text 3.2.3
Database Upgrade Assistant 7.2.2.1
Data preparation
- model view
  - text features 4.10.27
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 7.3.4
DM$VA 4.10.8, 4.10.11, 4.10.12, 4.10.16, 4.10.18, 4.10.22
DM$VB 4.10.10, 4.10.16, 4.10.18, 4.10.22
DM$VC 4.10.7, 4.10.9, 4.10.10, 4.10.11, 4.10.12, 4.10.14
DM$VD 4.10.8, 4.10.16, 4.10.18
DM$VE 4.10.20
DM$VF 4.10.16
DM$VG 4.10.7, 4.10.8, 4.10.9, 4.10.10, 4.10.11, 4.10.12, 4.10.14, 4.10.16, 4.10.18, 4.10.20, 4.10.22, 4.10.26
DM$VH 4.10.16, 4.10.18
DM$VI 4.10.7, 4.10.14, 4.10.16, 4.10.20
DM$VM 4.10.7, 4.10.16
DM$VN 4.10.8, 4.10.9, 4.10.11, 4.10.16, 4.10.20
DM$VO 4.10.7, 4.10.16
DM$VP 4.10.7, 4.10.10, 4.10.16, 4.10.26
DM$VR 4.10.16, 4.10.18
DM$VS 4.10.7, 4.10.8, 4.10.9, 4.10.10, 4.10.11, 4.10.12, 4.10.14, 4.10.16, 4.10.18, 4.10.20, 4.10.22
DM$VT 4.10.7, 4.10.9, 4.10.10, 4.10.11, 4.10.12, 4.10.14
DM$VV 4.10.10
DM$VW 4.10.7, 4.10.8, 4.10.9, 4.10.10, 4.10.11, 4.10.12, 4.10.14, 4.10.16, 4.10.18, 4.10.20, 4.10.22
downgrading 7.2.4

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 7.3.6
exporting 7.2.2.2, 7.3

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, 3.1.3, 4.2, 4.3

G

Generalized Linear Model 4.4.3
GLM 4.3
graphical user interface 1.1

I

importing 7.2.2.2, 7.3
installation
- Oracle Database 7.1.1
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
logistic regression 2.4, 4.2

M

machine learning
- database tuning for 7.1.2
- examples A
- privileges for 7.1.1, 7.4.1
- scoring 4.2, 5
machine learning for SQL
- privileges for A.2
machine learning for SQL models
- adding a comment 7.5, 7.5.1
- auditing 7.5.2
- object privileges 7.4.3
machine learning functions 4.1, 4.2
- supervised 4.2
- unsupervised 4.2
- used by examples A.1
machine learning models
- auditing 7.5.2
machine learning models for SQL
- adding a comment 2.1
- applying 7.4.3
- auditing 2.1
- changing the name 7.4.3
- data dictionary 2.2
- privileges for 2.1
- upgrading 7.2.2
- viewing model details 7.4.3
machine learning techniques 2.1
market basket data 3.4
MDL 4.4.3
memory 7.1.2
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 4.10
- association rules 4.10.1
- clustering algorithms 4.10.15
- CUR Matrix Decomposition 4.10.6
- Decision Tree 4.10.7
- EM 4.10.16
- Explicit Semantic Analysis 4.10.19
- Exponential Smoothing 4.10.26
- for binning 4.10.23
- for classification algorithms 4.10.5
- for frequent itemsets 4.10.2
- for global information 4.10.24
- for normalization and missing value handling 4.10.25
- for transactional itemsets 4.10.3
- for transactional rules and itemsets 4.10.4
- GLM 4.10.8
- k-Means 4.10.17
- Minimum Description Length 4.10.22
- MSET-SPRT 4.10.9
- Naive Bayes 4.10.10
- Neural Network 4.10.11
- Non-Negative Matrix Factorization 4.10.20
- O-Cluster 4.10.18
- Random Forest 4.10.12
- SVD 4.10.21
- SVM 4.10.13
- XGBoost 4.10.14
model detail views for Random Forest 4.10.12
models
- algorithms 4.3
- deploying 5.1
- partitions 2.2
- privileges for 7.4.1.1
- settings 2.2, 4.9
- testing 3.1.2
- training 3.1.2
- 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 7.4.3
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, 7.2.1
Oracle Data Pump 7.3
Oracle Machine Learning
- database support 7.1.1
- Installation 7.1.1
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, 7.1.2
partitioned model 4.8.4
- add partition 4.8.4.2.2
- build 4.8.4.1
- DDL implementation 4.8.4.2
- drop model 4.8.4.2.1
- drop partition 4.8.4.2.1
- scoring 4.8.4.3
partitions
- data dictionary 2.2
PGA 7.1.2
PL/SQL packages 2.3
PMML 7.3.7
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
preparing data
- using retail analysis data aggregates 3.5
prior probabilities 4.8.2
priors table 4.8.2
privileges 7.4.1
- for creating machine learning models 7.2.3
- for machine learning 7.1.1
- for OML4SQL examples A.2
- required for machine learning 7.4.1.1

R

RALG_BUILD_FUNCTION 4.8.5.2
RALG_BUILD_PARAMETER 4.8.5.2.1
RALG_DETAILS_FORMAT 4.8.5.4
RALG_DETAILS_FUNCTION 4.8.5.3
RALG_SCORE_FUNCTION 4.8.5.5
RALG_WEIGHT_FUNCTION 4.8.5.6
Random Forest 4.10.12, 4.2, 4.3, 4.4.3
REGISTER_ALGORITHM procedure 4.8.5.8
regression 2.1, 3.1.2, 3.1.3, 3.2.2, 4.2, 4.3
reverse transformations 3.2.6
R extensible language 4.3
R machine learning model
- settings 4.8.5

S

scoring 1.1, 2.1, 5, 7.1.2, 7.4.3
- data 3.1.2
- dynamic 1.3, 2.1, 5.5
- parallel execution 5.1
- privileges for 7.4.2
- requirements 3.1.3
- SQL functions 2.4, 2.5
- transparency 1.1
settings
- data dictionary 2.2
- table for specifying 4.1
SGA 7.1.2
Singular Value Decomposition 4.4.3
sparse data 3.6
SQL AUDIT 2.1, 7.5.2
SQL COMMENT 2.1, 7.5.1
SQL Developer 1.1
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
supported Database editions 7.1.1
Support Vector Machine 4.2, 4.3, 4.4.3
SVD 4.3
system privileges 7.4.2, A.2

T

target 3.2.2, 3.2.3, 3.2.4, 6.3
test data 3.1.2, 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, 3.1.3, 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, 3.1.3
- user-specified 3.1.3
transparency 3.2.6
trimming 4.5.4.4

U

upgrading 7.2.2
- exporting and importing 7.2.2.2
- pre-upgrade steps 7.2.1
- using Database Upgrade Assistant 7.2.2.1
users 7.1.1, A.2
- assigning machine learning privileges to 7.4.1.1
- creating 7.4.1
- privileges for machine learning 7.4.1
- privileges for machine learning for SQL 7.2.3

W

weights 4.8.3
what are the machine learning SQL API packages 2.3.1
windsorize 4.5.4.4

X

XFORM 2.3.2.1
XFORMS
- data dictionary 2.2
XGBoost 4.2, 4.3
- model detail views 4.10.14
XG Boost 4.4.3