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Index

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

A

ADP 5.2.3.2
Advanced Analytics option 8.1.1, A.2
algorithms 5.1, 5.2.2
- parallel execution 8.1.3
Algorithms
- About Algorithm Meta Data Registration 5.3.5.8
- Algorithm Meta Data Registration 5.3.5.8
ALL_MINING_MODEL_ATTRIBUTES 2.2
ALL_MINING_MODEL_SETTINGS 2.2, 5.3.4
ALL_MINING_MODEL_VIEWS 2.2
ALL_MINING_MODEL_XFORMS 2.2
ALL_MINING_MODELS 2.2
anomaly detection 2.1, 3.1.3, 5.2.1, 5.2.2, 6.7
APPLY 6.1
Apriori 3.4, 4.3.3, 5.2.2
- example: calculating aggregates 3.5.1
association rules 5.2.1, 5.2.2
Association Rules 5.4.1
attribute importance 2.1, 5.2.1, 5.2.2
attributes 3.1.1, 3.2, 7.4
- categorical 3.2.3, 7.1
- 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, 7.1
- subname 3.2.5
- target 3.2.2
- text 3.2.3
- unstructured text 7.2
attribute specification 4.4.1, 7.6
AUDIT 8.4.2, 8.5.2
Automatic Data Preparation 1.1, 3.1.3, 4.3

B

binning 4.3.1
- equi-width 4.4.4.1
- quantile 4.4.4.1
- supervised 4.3.1, 4.4.4.1
- top-n frequency 4.4.4.1
build data 3.1.2

C

case ID 3.1, 3.1.1, 3.2.4, 6.7
case table 3.1, 4.2
categorical attributes 7.1
chopt utility 8.1.2
classification 2.1, 3.1.2, 3.1.3, 3.2.2, 5.2.1, 5.2.2
Classification Algorithm 5.4.5
class weights 5.3.3
clipping 4.4.4.3
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, 5.2.2
COMMENT 8.4.2
cost matrix 5.3.1, 6.6, 8.4.3
cost-sensitive prediction 6.6
CUR Decomposition 5.2.2
CUR Matrix Decomposition 5.2.1

D

data
- categorical 3.2.3
- dimensioned 3.3.2
- for sample programs A.3
- market basket 3.4
- missing values 3.6
- multi-record case 3.3.2
- nested 3.1.1
- numerical 3.2.3
- preparation 4
- 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 mining
- applications of 1.1
- database tuning for 8.1.3
- privileges for 8.1.1, 8.4.1, A.2
- sample programs A
- scoring 5.2.1, 6
Data Mining with SQL
- FEATURE_COMPARE
  - ESA 1.5
Data preparation
- model view
  - text features 7.3
data types 3.1.1, 4.2.2
- nested 3.3
DBMS_DATA_MINING 2.3, 5.2.1
DBMS_DATA_MINING_TRANSFORM 2.3, 2.3.2
DBMS_PREDICTIVE_ANALYTICS 1.3, 2.3, 2.3.3
Decision Tree 4.3.3, 5.2.1, 5.2.2, 6.4
desupported features
- Java API 8.2.1
directory objects 8.3.3
DMEIDMSYS 8.2.2.2.1
downgrading 8.2.4

E

Expectation Maximization 4.3.3
EXPLAIN 2.3.3
Explicit Semantic Analysis 5.2.1, 5.2.2, 5.4.17
Exponential Smoothing 5.2.2
- ESM 5.2.1
Export and Import
- serialized models 8.3.5
exporting 8.2.2.2, 8.3

F

FEATURE_COMPARE 2.4
FEATURE_DETAILS 2.4
FEATURE_ID 2.4
FEATURE_SET 2.4
FEATURE_VALUE 2.4
feature extraction 2.1, 3.1.3, 5.2.1, 5.2.2
Frequent Itemsets 5.4.2

G

Generalized Linear Models 4.3.3
GLM 5.2.2
graphical user interface 1.1

I

importing 8.2.2.2, 8.3
installation
- Oracle Database 8.1.1, A.2
- Oracle Database Examples A.2
- sample data mining programs A.2
- sample schemas A.2

K

k-Means 4.3.3, 5.2.1, 5.2.2

L

linear regression 2.4, 5.2.1
logistic regression 2.4, 5.2.1

M

market basket data 3.4
MDL 4.3.3
memory 8.1.3
Minimum Description Length 4.3.3, 5.2.2, 5.4.20
mining functions 2.1, 5.1, 5.2.1
- supervised 5.2.1
- unsupervised 5.2.1
mining models
- adding a comment 2.1, 8.5, 8.5.1
- applying 8.4.3
- auditing 2.1, 8.5.2
- changing the name 8.4.3
- created by sample programs A.1
- data dictionary 2.2
- object privileges 8.4.3
- privileges for 2.1
- upgrading 8.2.2
- viewing model details 8.4.3
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 details
- binning 5.4.21
Model Detail View
- model view 5.4.1, 5.4.2, 5.4.3, 5.4.4, 5.4.9, 5.4.17, 5.4.18
  - Clustering algorithm 5.4.13
  - CUR Matrix Decomposition 5.4.6
  - ESM 5.4.24
  - Exponential Smoothing 5.4.24
  - global 5.4.22
  - MDL 5.4.5
  - Neural Network 5.4.10
  - Random Forest 5.4.11
Model Detail Views 5.4
- model view
  - Decision Tree 5.4.7
  - EM 5.4.14
  - GLM 5.4.8
  - KM 5.4.15
  - MDL 5.4.20
  - OC 5.4.16
  - SVD 5.4.19
  - SVM 5.4.12
models
- algorithms 5.2.2
- created by sample programs A.1
- deploying 6.1
- partitions 2.2
- privileges for 8.4.1.1
- settings 2.2, 5.3.4
- testing 3.1.2
- training 3.1.2
- transparency 1.1
- XFORMS 2.2
model signature 3.2.4

N

Naive Bayes 4.3.3, 5.2.1, 5.2.2, 5.4.9
nested data 3.3, 7.4
Neural Network 5.2.1, 5.2.2
NMF 5.2.2
Non-Negative Matrix Factorization 4.3.3, 5.2.1, 5.4.18
normalization 4.3.2
- min-max 4.4.4.2
- scale 4.4.4.2
- z-score 4.4.4.2
Normalization view
- model view
  - missing value handling 5.4.23
numerical attributes 7.1

O

object privileges 8.4.3
O-Cluster 3.3, 4.3.3, 5.2.1, 5.2.2
One-Class SVM 5.2.1
ORA_DM_PARTITION_NAME ORA 2.4
Oracle Data Miner 1.1, 8.2.1, 8.2.1.2
Oracle Data Miner Classic 8.2.1
Oracle Data Pump 8.3
Oracle Text 7.2
outliers 4.4.4.3, 4.4.4.4

P

parallel execution 6.1, 8.1.3
Partitioned model 5.2.4
- partitioned model scoring 5.2.4.3
Partitioned Model
- add partition 5.2.4.2.2
- DDL implementation 5.2.4.2
- drop model 5.2.4.2.1
- drop partition 5.2.4.2.1
- Partitioned Model Build 5.2.4.1
partitions
- data dictionary 2.2
PGA 8.1.3
PL/SQL packages 2.3
PMML 8.3.6
PREDICTION 1.2, 1.3, 2.4, 6.5
- GROUPING hint 6.3.4
PREDICTION_BOUNDS 2.4
PREDICTION_COST 2.4
PREDICTION_DETAILS 2.4, 6.5
PREDICTION_PROBABILITY 1.3, 2.4, 6.4
PREDICTION_SET 2.4
predictive analytics 1.1, 1.3, 2.1
Preparing the Data
- Using Retail Analysis Data
  - Aggregates 3.5
prior probabilities 5.3.2
priors table 5.3.2
privileges 8.3.2, 8.4.1, 8.4.1.1
- for creating mining models 8.2.3
- for data mining 8.1.1, 8.3.2
- for data mining sample programs A.2
- for exporting and importing 8.3.2
- required for data mining 8.4.1.1

R

Random Forest 5.2.1, 5.2.2
regression 2.1, 3.1.2, 3.1.3, 3.2.2, 5.2.1, 5.2.2
reverse transformations 3.2.6
R Extensible 5.2.2
R mining model
- settings 5.3.5

S

sample programs 1.1, A.1
- configuration scripts 8.4.1
- data used by A.3
- directory listing of A.1
- installing A.2
- models created by A.1
- Oracle Database Examples A.2
- requirements A.2
- sample schemas A.2
scoring 1.1, 2.1, 6, 8.1.3, 8.4.3
- data 3.1.2
- dynamic 1.3, 2.1, 6.5
- parallel execution 6.1
- privileges for 8.4.2
- requirements 3.1.3
- SQL functions 2.4
- transparency 1.1
Scoring Engine 8.2.2.1.1
settings
- data dictionary 2.2
- table for specifying 5.1
SGA 8.1.3
Singular Value Decomposition 4.3.3, 5.4.19
sparse data 3.6
SQL AUDIT 2.1, 8.5.2
SQL COMMENT 2.1, 8.5.1
SQL data mining functions 2.4
SQL Developer 1.1
STACK 2.3.2.1, 4.4.2.2
Static Dictionary Views
- ALL_MINING_MODEL_VIEWS 2.2.5
Support Vector Machine 4.3.3, 5.2.1, 5.2.2
SVD 5.2.2
system privileges 8.4.2, A.2

T

target 3.2.2, 3.2.3, 3.2.4, 7.4
test data 3.1.2, 5.1
text attributes 7.4, 7.6
text mining 2.3.2.1, 7
text policy 7.5
text terms 7.2
time series 5.2.1, 5.2.2
training data 5.1
transactional data 3.1, 3.3.2, 3.4
Transactional Itemsets 5.4.3
Transactional rule 5.4.4
transformations 2.3.2, 3.1.3, 3.2.1, 3.2.6, 5.1, 5.2.3
- attribute-specific 2.3.2, 2.3.2.1
- embedded 2.3.2, 2.3.2.1, 3.1.3, 4
- user-specified 3.1.3
transparency 3.2.6
trimming 4.4.4.4

U

upgrading 8.2.2
- exporting and importing 8.2.2.2
- from Release 10g 8.2.2.1.1
- from Release 11g 8.2.2.1.2
- pre-upgrade steps 8.2.1
- using Database Upgrade Assistant 8.2.2.1
Usage scripts 5.3.5.7
users 8.1.1, 8.3.2, A.2
- assigning data mining privileges to 8.4.1.1
- creating 8.4.1
- privileges for data mining 8.2.3, 8.4.1

W

weights 5.3.3
windsorize 4.4.4.4

X

XFORM 2.3.2.1
XFORMS
- data dictionary 2.2