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

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, 2.4.3.1, 3.1, 3.1, 3.1.1, 3.2.4, 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

ODMS_ITEM_ID_COLUMN_NAME, 3.3.4

ODMS_ITEM_VALUE_COLUMN_NAME, 3.3.4

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

T

target, 3.2.2, 3.2.4, 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, Preface, 3.1, 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