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

See Automatic Data Preparation

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

batch, 6.5

real time, 6.2

B

build data, 3.1.2

BuildSettings object, 2.4.3.4

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

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

constants, 5.3.1

cost matrix, 6.4

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

for data mining, 3

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

data dictionary views, 2.2

Data Mining Engine, 2.4.1, 2.4.3.4, 2.4.3.4

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

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

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

dmsh.sql, 4.2

dmtxtfe.sql, 4.2

E

embedded transformations, 2.1.2, 3.1.2, 5.3.2

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

GET_MODEL_DETAILS, 2.1.1.1, 5.4

GET_MODEL_DETAILS_XML, 6.3.1.4

GLM, 5.2.2, 5.4

See Generalized Linear Models

I

index preference, 4.1

J

Java API, 2.4, 7

K

k-Means, 5.2.2, 5.3.1, 5.4

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

Minimum Description Length, 5.2.2, 5.2.2

mining model schema objects, 2.2, 5.5

missing value treatment, 3.4.3

missing values, 3.4

Model, 2.4.3.4

model details, 3.2.6, 5.1, 5.4

model signature, 3.2.4

models

algorithms, 5.2.2

deploying, 6.2

privileges for, 5.5.2

scoring, 6

settings, 5.2.6

steps in creating, 5.1

N

Naive Bayes, 5.2.2, 5.3.1, 5.4

nested data, 3.3, 4.3, 4.4.6

NMF, 5.4

See Non-Negative Matrix Factorization

Non-Negative Matrix Factorization, 5.3.1

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

Oracle Text, 4.1

outliers, 1.1.3.1

P

PhysicalDataSet, 2.4.3.1

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

PREP_AUTO, 5.3.2

privileges, 5.5.2

PROFILE, 1.3, 2.1.3

R

regression, 5.2.2, 5.2.2, 5.3.1

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

scoping of attribute name, 3.2.5

scoring, 1.1.1, 2.1.1.2, 2.3, 6

batch, 6.5

data, 3.1.2

saving results, 6.3.4

T

target, 3.2.2, 3.2.4, 3.2.4.1

test data, 3.1.2

text mining, 4, 4

text transformation, 4

Java, 4.1

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

transparency, 3.2.6, 5.4

U

unsupervised mining functions, 5.3.1