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Oracle® Data Mining Application Developer's Guide
11g Release 1 (11.1)

B28131-04
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Index

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

A

ADD_COST_MATRIX, 6.4
ADP, 2.1.2, 3.1.2, 5.3.2, 7.3.3
ALGO_NAME, 5.2.1
algorithms, 5.2.2
ALL_MINING_MODEL_ATTRIBUTES, 2.2, 3.2.4.1
ALL_MINING_MODEL_SETTINGS, 2.2, 5.2.6
ALL_MINING_MODELS, 2.2
anomaly detection, 1.1.3, 5.2.2, 5.3.1, 5.3.1, 6.5
apply, 2.1.1.2, 7.3.9
batch, 6.5
real time, 6.2
ApplySettings, 2.4.3.6, 7.3.9
ApplySettings object, 2.4.3.6, 2.4.3.6
Apriori, 5.2.2, 5.2.2
association rules, 5.2.2, 5.3.1, 5.4
asynchronous execution of mining tasks, 7.2.4.4, 7.3.4
attribute importance, 3.2.6, 5.2.2, 5.3.1, 5.4
attribute name, 3.2.5
attribute subname, 3.2.5
attributes, 3, 3.2, 7.3.1
Automatic Data Preparation
See ADP

B

binning, Preface, 7.3.12.1, 7.3.14.1, 7.3.14.1
build data, 3.1.2
BuildSettings, 2.4.3.2, 7.3.2, 7.3.6
BuildSettings object, 2.4.3.2, 2.4.3.4
BuildTask object, 7.3.6, 7.3.7

C

case ID, 6.5.1
case table, 3
catalog views, 2.2
categorical, 3.2.3
centroid, 5.4
classes, 3.2.3
classification, 5.2.2, 5.2.2, 5.3.1
CLASSPATH, 7.1
clipping, 7.3.12.1, 7.3.14.3, 7.3.14.3
CLUSTER_ID, 1.1.1, 2.3, 6.3.2.1
CLUSTER_PROBABILITY, 2.3, 6.3.2.2
CLUSTER_SET, 2.3, 6.3.2.3
clustering, 2.3, 5.2.2, 6.3.2
collection types, 3.3.1, 4.3
Connection, 7.2.4.2
Connection object, 2.4.3, 2.4.3.3
ConnectionSpec, 7.2.3
constants, 5.3.1
cost matrix, 6.4, 7.3.10
costs, 6.3.1.3, 6.4
CREATE_MODEL, 2.1.1.1, 5.3
CTXSYS.DRVODM, 4.1

D

data
dimensioned, 3.3.2
market basket, Preface
missing values, 3.4
multi-record case, 3.3.2
nested, 3.3
preparing, 2.1.2
sparse, 3.4
transactional, 3.3.2, 3.3.4
transformations, 5.3.2, 7.3.12, 7.3.14
data dictionary views, 2.2
Data Mining Engine, 2.4.1, 2.4.3.4, 2.4.3.4, 7.2
data preparation, 2.1.2, 7.3.14
data types, 3.1.1
DBMS_DATA_MINING, 2.1, 5.3
DBMS_DATA_MINING_TRANSFORM, 2.1, 2.1.2, 5.3.2
DBMS_PREDICTIVE_ANALYTICS, 1.3, 2.1, 2.1.3
DBMS_SCHEDULER, 2.4.3.3, 7.2.4.4, 7.3.4
Decision Tree, 2.3, 5.2.2, 5.3.1, 5.4, 6.3, 6.3.1.4
demo programs, 5.5.3
dimensioned data, 3.3.2
discretization, 7.3.14.1
DM_NESTED_CATEGORICALS, 3.2.3, 3.3.1.2
DM_NESTED_NUMERICALS, 3.2.3, 3.3.1.1, 3.3.3, 4.3, 4.3, 4.4.6
DME
See Data Mining Engine
dmsh.sql, 4.2
DMSYS schema
See desupported features
dmtxtfe.sql, 4.2

E

embedded transformations, 2.1.2, 3.1.2, 5.3.2, 7.3.12
Execute method, 2.4.3.3
EXPLAIN, 2.1.3

F

feature extraction, 2.3, 5.2.2, 5.3.1, 6.3.3, 6.3.3
FEATURE_EXPLAIN table function, 4.1, 4.4.1, 4.4.5.1
FEATURE_ID, 2.3, 6.3.3.1
FEATURE_PREP table function, 4.1, 4.4.1, 4.4.4.1
FEATURE_SET, 2.3, 6.3.3.3
FEATURE_VALUE, 2.3, 6.3.3.2

G

Generalized Linear Models
See GLM
GET_MODEL_DETAILS, 2.1.1.1, 5.4
GET_MODEL_DETAILS_XML, 6.3.1.4
GLM, 5.2.2, 5.4

I

index preference, 4.1

J

Java API, 1, 1, 2.4, 7
connecting to the Data Mining Engine, 7.2
connecting using JDBC, 7.2.2
data, 2.4.3.1, 7.3.1
data transformations, 7.3.12, 7.3.14
Database Scheduler, 7.2.4.4
design overview, 7.3
setting up the development environment, 7.1
text transformation, 7.3.14.4
JDBC, 7.2.2
JDM, 2.4, 7
named objects, 2.4.3, 7.3
Oracle extensions to, 2.4.2

K

k-Means, 5.2.2, 5.3.1, 5.4, 7.3.14.2

L

linear regression, 2.3, 5.3.1
logistic regression, 2.3, 5.3.1

M

market basket data, 3.3.4, 3.3.4
MDL, 5.2.2
Minimum Description Length
See MDL
mining model schema objects, 2.2, 5.5
missing value treatment, Preface, 3.4.3
missing values, 3.4
Model, 2.4.3.4
model details, 3.2.6, 5.1, 5.4, 7.3.7, 7.3.7
model signature, 3.2.4
models
algorithms, 5.2.2
building, 7.3.6, 7.3.6
deploying, 6.2
privileges for, 5.5.2
scoring, 6, 7.3.9, 7.3.9
settings, 5.2.6, 7.3.2
steps in creating, 5.1
testing, 7.3.8, 7.3.8

N

Naive Bayes, 5.2.2, 5.3.1, 5.4
nested data, 3.3, 4.3, 4.4.6, 7.3.14.4
NMF, 5.3.1, 5.4, 7.3.14.2
Non-Negative Matrix Factorization
See NMF
normalization, Preface, 7.3.12.1, 7.3.14.2, 7.3.14.2
numerical, 3.2.3

O

O-Cluster, 5.2.2, 5.3.1
One-Class SVM, 1.1.3, 5.3.1, 5.3.1
OraBinningTransformation, 7.3.14.1
Oracle Text, 4.1
OraClippingTransformation, 7.3.14.3
OraConnectionFactory, 7.2.1.1
OraExplainTask, 7.3.13
OraNormalizeTransformation, 7.3.14.2
OraPredictTask, 7.3.13
OraProfileTask, 7.3.13
OraTextTransformation, 7.3.14.4
OraTransformationFactory, 7.3.12.1
OraTransformationSequence, 7.3, 7.3.12.2
outliers, 1.1.3.1

P

PhysicalDataSet, 2.4.3.1, 7.3.1, 7.3.6
PhysicalDataSet object, 2.4.3.1
PIPELINED, 3.2.6
PL/SQL API, 1, 1, 2.1
PREDICT, 2.1.3
PREDICTION, 1.1.2, 1.1.3.3, 2.3, 6.3.1.1, 6.4
PREDICTION_BOUNDS, 2.3, 6.3.1.2
PREDICTION_COST, 2.3, 6.3.1.3
PREDICTION_DETAILS, 1.2, 2.3
PREDICTION_PROBABILITY, 1.1.1, 1.1.2, 1.1.3.1, 2.3, 6.3, 6.3.1.5
PREDICTION_SET, 2.3, 6.3.1.6
predictive analytics, 1.3, 2.1.3, 7.3.13
PREP_AUTO, 5.3.2
prior probabilities, 7.3.11
privileges, 5.5.2
PROFILE, 1.3, 2.1.3

R

regression, 5.2.2, 5.2.2, 5.3.1
RegressionTestMetrics, 7.3.8
REMOVE_COST_MATRIX, 6.4
reverse transformations, 3.2.4.1, 3.2.6, 3.2.6, 5.4
rules, 6.3.1.4

S

sample programs, 5.5.3
Scheduler, 7.2.4.4, 7.3.4
scoping of attribute name, 3.2.5
scoring, 1.1.1, 2.1.1.2, 2.3, 6, 7.3.9
batch, 6.5
data, 3.1.2
Java API, 7.3.9
saving results, 6.3.4
settings table, 2.4.3.2, 7.3.2
sparse data, 3.4, 3.4
SQL AUDIT, 5.5
SQL COMMENT, 5.5
SQL data mining functions, 1, 2.3
STACK, 2.1.2, 5.3.2
supermodels, 3.1.2
supervised mining functions, 5.3.1
Support Vector Machines
See SVM
SVM, 5.2.2, 5.3.1, 5.3.1, 5.4, 7.3.14.2
SVM_CLASSIFIER index preference, 4.1, 4.4.1, 4.4.3
synchronous execution of mining tasks, 7.2.4.4, 7.3.4

T

target, 3.2.2, 3.2.4.1
Task, 2.4.3.3, 7.3.4
test data, 3.1.2
TestMetrics, 2.4.3.5
TestMetrics object, 2.4.3.5
TestTask, 7.3.8
text mining, 4, 4
text transformation, 4
Java, 4.1, 7.3.14.4
PL/SQL, 4.1
transactional data, 3.3.2, 3.3.2, 3.3.4, 3.3.4
transformation list, 5.3.2
transformations, 2.1.2, 3, 3.2.4.1, 3.2.6, 3.2.6, 5.3.2, 7.3.12
TransformationSequence, 2.4.3.7
TransformationSequence object, 2.4.3.7
transparency, 3.2.6, 5.4

U

unsupervised mining functions, 5.3.1