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Oracle® Data Mining User's Guide
12c Release 1 (12.1)

E17693-15
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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

ADD_COST_MATRIX, 2.3.1.1
ADP, 5.2.3.2
See Automatic Data Preparation
Advanced Analytics option, 8.1.1, A.2
algorithms, 5.1, 5.2.2, 5.2.2
parallel execution, 8.1.3
ALL_MINING_MODEL_ATTRIBUTES, 2.2, 3.2.2
ALL_MINING_MODEL_SETTINGS, 2.2, 5.3.4
ALL_MINING_MODELS, 2.2
ALTER ANY MINING MODEL, 8.4.2
ALTER MINING MODEL, 8.4.3
ALTER_REVERSE_EXPRESSION, 2.3.1.1
anomaly detection, 2.1, 3.1.3, 5.2.1, 5.2.1, 5.2.2, 6.7
APPLY, 6.1, 6.7
See also scoring
Apriori, 3.4, 4.3.4, 5.2.2, 5.2.2
association rules, 5.2.1, 5.2.1, 5.2.2
attribute importance, 2.1, 3.2.6, 5.2.1, 5.2.2
attribute specification, 4.4.1, 7.5, 7.5
attributes, 3.1.1, 3.2, 7.3
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
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
class weights, 5.3.3
classification, 2.1, 3.1.2, 3.1.3, 3.2.2, 5.2.1, 5.2.2, 5.2.2
clipping, 4.3.3
CLUSTER_DETAILS, 1.4, 2.4
CLUSTER_DISTANCE, 2.4
CLUSTER_ID, 1.4, 2.4, 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
COMMENT ANY MINING MODEL, 8.4.2, 8.5.1
COMPATIBLE parameter, 8.2.3
cost matrix, 2.3.1.1, 5.3.1, 6.6, 8.4.3
cost-sensitive prediction, 6.6
CREATE ANY DIRECTORY, 8.3.3
CREATE ANY MINING MODEL, 8.4.2
CREATE MINING MODEL, 8.2.3, 8.4.2
CREATE_MODEL, 2.1, 2.3.1.1, 5.2
CREATE_POLICY, 7.4

D

data
categorical, 3.2.3
dimensioned, 3.3.2
for sample programs, A.3
market basket, 3.4
missing values, 3.5, 3.5
multi-record case, 3.3.2
nested, 3.1.1
numerical, 3.2.3
preparation, 4
single-record case, 3.1
sparse, 3.5
transactional, 3.4
unstructured text, 3.2.3
data mining
applications of, 1.1
database tuning for, 8.1.3
DDL for, 2.3.1.1
privileges for, 8.1.1, 8.4.1, A.2
sample programs, A, A
scoring, 5.2.1, 6
data types, 3.1.1, 4.2.2
BFILE, 7.3
BLOB, 7.3
CHAR, 7.3
CLOB, 7.3
DM_NESTED_BINARY_DOUBLES, 3.1.1
DM_NESTED_BINARY_FLOATS, 3.1.1
DM_NESTED_CATEGORICALS, 3.1.1
DM_NESTED_NUMERICALS, 3.1.1, 3.4.1
nested, 3.3
VARCHAR2, 7.3
Database Upgrade Assistant, 8.2.2.1, 8.2.2.1
DBA_MINING_MODELS, 8.2.3
DBMS_DATA_MINING, 2.1, 2.3, 2.3.1, 5.1, 5.2, 5.2.1
DBMS_DATA_MINING_TRANSFORM, 2.3, 2.3.2, 2.3.2
DBMS_PREDICTIVE_ANALYTICS, 1.3, 2.3, 2.3.3, 2.3.3
Decision Tree, 4.3.4, 5.2.1, 5.2.2, 6.4
demo programs
See sample programs
desupported features
Java API, 8.2.1
directory objects, 8.3.3
discretization
See binning
DMEIDMSYS, 8.2.2.2.1
dmshgrants.sql, A.2, A.2
dmsh.sql, A.2, A.2, A.3
DMSYS, 8.2.2.1.1
downgrading, 8.2.4, 8.2.4
DROP ANY MINING MODEL, 8.4.2
DROP_MODEL, 2.3.1.1, 8.2.1.1, 8.2.4

E

EXPDP, 8.3.1
Expectation Maximization, 4.3.4
EXPLAIN, 2.3.3, 2.3.3
exporting, 8.2.2.2, 8.3

F

feature extraction, 2.1, 3.1.3, 5.2.1, 5.2.2
FEATURE_DETAILS, 2.4
FEATURE_ID, 2.4
FEATURE_SET, 2.4
FEATURE_VALUE, 2.4

G

Generalized Linear Models, 4.3.4
GLM, 5.2.2
See Generalized Linear Models
graphical user interface, 1.1

I

IMPDP, 8.3.1
importing, 8.2.2.2, 8.3
INIT.ORA, 8.1.3
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, 4.3.4, 5.2.1, 5.2.2

L

lexer, 7.4
linear regression, 2.4, 5.2.1
logistic regression, 2.4, 5.2.1

M

market basket data, 3.4, A.3
MAX_FEATURES, 7.5
MDL
See Minimum Description Length
memory, 8.1.3
MEMORY_TARGET, 8.1.3
Minimum Description Length, 4.3.4, 5.2.2, 5.2.2
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, 8.4.3
auditing, 2.1, 8.5.2, 8.5.2
changing the name, 8.4.3
created by sample programs, A.1
data dictionary, 2.2
object privileges, 8.4.3, 8.4.3
privileges for, 2.1
SQL DDL, 2.3.1.1
upgrading, 8.2.2
viewing model details, 8.4.3
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, 2.3.1.1, 3.2.6
model signature, 3.2.4
models
algorithms, 5.2.2
created by sample programs, A.1
deploying, 6.1
privileges for, 8.4.1.1
settings, 2.2, 5.3.4
testing, 3.1.2
training, 3.1.2
transparency, 1.1

N

Naive Bayes, 4.3.4, 5.2.1, 5.2.2
nested data, 3.3, 7.3
NMF
See Non-Negative Matrix Factorization
Non-Negative Matrix Factorization, 4.3.4, 5.2.1
normalization, 4.3.2
min-max, 4.4.4.2
scale, 4.4.4.2
z-score, 4.4.4.2
numerical attributes, 7.1

O

object privileges, 8.4.3, 8.4.3
O-Cluster, 3.3, 4.3.4, 5.2.1, 5.2.2
ODMS_ITEM_ID_COLUMN_NAME, 3.4
ODMS_ITEM_VALUE_COLUMN_NAME, 3.4
ODMS_TEXT_MAX_FEATURES, 7.3
ODMS_TEXT_POLICY_NAME, 7.3
One-Class SVM, 5.2.1
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, 4.3.3, 4.4.4.3

P

parallel execution, 6.1, 8.1.3
PGA, 8.1.3
PL/SQL packages, 2.3
PMML, 8.3.5, 8.3.5
POLICY_NAME, 7.5
PREDICT, 2.3.3
PREDICTION, 1.2, 1.3, 2.4, 6.5
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
PREP_AUTO, 5.2.3.2
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
PROFILE, 2.3.3

R

regression, 2.1, 3.1.2, 3.1.3, 3.2.2, 5.2.1, 5.2.2, 5.2.2
REMOVE_COST_MATRIX, 2.3.1.1
RENAME_MODEL, 2.3.1.1
reverse transformations, 2.3.1.1, 3.2.6

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
See also apply
SQL functions, 2.4
transparency, 1.1
Scoring Engine, 8.2.2.1.1
SELECT ANY MINING MODEL, 8.4.2
SELECT MINING MODEL, 8.4.3
SET_TRANSFORM, 2.3.2
settings
data dictionary, 2.2
table for specifying, 5.1
SGA, 8.1.3
Singular Value Decomposition, 4.3.4
sparse data, 3.5, 3.5
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, 4.4.2.2
stoplist, 7.4
Support Vector Machine, 4.3.4, 5.2.1, 5.2.2
SVM
See Support Vector Machine
system privileges, 8.4.2, A.2

T

target, 3.2.2, 3.2.3, 3.2.4, 7.3
test data, 3.1.2, 5.1
text attributes, 7.3, 7.5
text mining, 2.3.2, 7
text policy, 7.4
text terms, 7.2
TOKEN_TYPE, 7.5
training data, 5.1
transactional data, 3.1, 3.3.2, 3.3.2, 3.3.2, 3.4
transformations, 2.3.2, 3.1.3, 3.2.1, 3.2.6, 3.2.6, 5.1, 5.2.3
attribute-specific, 2.3.2, 2.3.2
embedded, 2.3.2, 2.3.2, 3.1.3, 4
reverse, 2.3.1.1
user-specified, 3.1.3
transparency, 3.2.6
trimming, 4.4.4.3

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
USER_MINING_MODEL_ATTRIBUTES, 2.2.2
USER_MINING_MODEL_SETTINGS, 2.2.3
USER_MINING_MODELS, 2.2.1
users, 8.1.1, 8.3.2, 8.3.2, A.2
assigning data mining privileges to, 8.4.1.1
creating, 8.4.1, 8.4.1
privileges for data mining, 8.2.3, 8.4.1

W

weights, 5.3.3
windsorize, 4.4.4.3

X

XFORM, 2.3.2