Index

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

A

ADD_COST_MATRIX, 4.4

ADP, 2.1.2, 3.3.2, 5.1.2

ALGO_NAME, 3.2

algorithms, 3.2.2

ALL_MINING_MODEL_ATTRIBUTES, 2.2, 5.2.4.1

ALL_MINING_MODEL_SETTINGS, 2.2, 3.2.3

ALL_MINING_MODELS, 2.2

anomaly detection, 1.1.3, 3.2.2, 3.3.1, 3.3.1, 4.5

APPLY, 2.1.1.2, 2.1.1.2, 2.3, 4.5, 7.3.9

ApplySettings object, 7.3.9

Apriori, 3.2.2, 3.2.2

association rules, 3.2.2, 3.3.1, 3.4

attribute importance, 3.2.2, 3.3.1, 3.4, 5.2.6

attribute name, 5.2.5

attribute subname, 5.2.5

attributes, 5, 5.2

Automatic Data Preparation

See ADP

B

binning, 7.3.14.1

build data, 5.1.2

BuildSettings object, 7.3.2

BuildTask object, 7.3.7

C

case ID, 4.5.1

case table, 5

catalog views, 2.2

categorical, 5.2.3

centroid, 3.4

classes, 5.2.3

classification, 3.2.2, 3.3.1

CLASSPATH, 7.1

clipping, 7.3.14.3

CLUSTER_ID, 1.1.1, 2.3, 4.3.2.1

CLUSTER_PROBABILITY, 2.3, 4.3.2.2

CLUSTER_SET, 2.3, 4.3.2.3

clustering, 2.3, 3.2.2, 4.3.2

collection types, 5.3.1, 6.3

constants, 3.3.1

cost matrix, 4.4, 7.3.10

costs, 4.3.1.3, 4.4

CREATE_MODEL, 2.1.1.1, 3.1, 3.3

CTXSYS.DRVODM, 6.1

D

data

dimensioned, 5.3.2

missing values, 5.4

multi-record case, 5.3.2

nested, 5.3

preparing, 2.1.2

sparse, 5.4

transactional, 5.3.2, 5.3.4

transformations, 3.3.2

data dictionary views, 2.2

Data Mining Engine, 7.2

data preparation, 2.1.2, 3.1, 7.3.14

data types, 5.1.1

DBMS_DATA_MINING, 2.1, 3.3

DBMS_DATA_MINING_TRANSFORM, 2.1, 2.1.2, 3.3.2

DBMS_PREDICTIVE_ANALYTICS, 1.3, 2.1, 2.1.3

Decision Tree, 2.3, 3.2.2, 3.3.1, 3.4, 4.3, 4.3.1.4

demo programs, 3.5.3

dimensioned data, 5.3.2

DM_NESTED_CATEGORICALS, 5.2.3, 5.3.1.2

DM_NESTED_NUMERICALS, 5.2.3, 5.3.1.1, 5.3.3, 6.3, 6.3, 6.4.6

dmsh.sql, 6.2

dmtxtfe.sql, 6.2

E

embedded transformations, 2.1.2, 3.3.2, 5.1.2

EXPLAIN, 2.1.3

F

feature extraction, 2.3, 3.2.2, 3.3.1, 4.3.3, 4.3.3

FEATURE_EXPLAIN table function, 6.1, 6.4.1, 6.4.5.1

FEATURE_ID, 2.3, 4.3.3.1

FEATURE_PREP table function, 6.1, 6.4.1, 6.4.4.1

FEATURE_SET, 2.3, 4.3.3.3

FEATURE_VALUE, 2.3, 4.3.3.2

G

Generalized Linear Models

See GLM

GET_MODEL_DETAILS, 2.1.1.1, 3.4

GET_MODEL_DETAILS_XML, 4.3.1.4

GLM, 3.2.2, 3.4

I

index preference, 6.1

J

Java API, 1, 1, 2.4, 7

data, 7.3.1

data transformations, 7.3.14

setting up the development environment, 7.1

text transformation, 7.3.14.4

JDM, 2.4, 7

K

k-Means, 3.2.2, 3.3.1, 3.4, 7.3.14.2

L

linear regression, 2.3, 3.3.1

logistic regression, 2.3, 3.3.1

M

market basket data, 5.3.4, 5.3.4

MDL, 3.2.2

Minimum Description Length

See MDL

mining model schema objects, 2.2, 3.5

missing value treatment, 5.4.3

missing values, 5.4

model details, 3.1, 3.4, 5.2.6, 7.3.7

model signature, 5.2.4

models

algorithms, 3.2.2

building, 7.3.6

deploying, 4.2

privileges for, 3.5.2

scoring, 7.3.9

settings, 3.2, 3.2.3, 7.3.2

steps in creating, 3.1

testing, 7.3.8

N

Naive Bayes, 3.2.2, 3.3.1, 3.4

nested data, 5.3, 6.3, 6.4.6, 7.3.14.4

NMF, 3.3.1, 3.4, 6.1, 7.3.14.2

Non-Negative Matrix Factorization

See NMF

normalization, 7.3.14.2

numerical, 5.2.3

O

O-Cluster, 3.2.2, 3.3.1

One-Class SVM, 1.1.3, 3.3.1, 3.3.1

OraBinningTransformation, 7.3.14.1

Oracle Text, 6, 6.1

OraClippingTransformation, 7.3.14.3

OraNormalizeTransformation, 7.3.14.2

OraTextTransformation, 7.3.14.4

outliers, 1.1.3.1

P

PIPELINED, 5.2.6

PL/SQL API, 1, 1, 2.1

PREDICT, 2.1.3

PREDICTION, 1.1.2, 1.1.3.3, 2.3, 4.3.1.1, 4.4

PREDICTION_BOUNDS, 2.3

PREDICTION_COST, 2.3, 4.3.1.3

PREDICTION_DETAILS, 1.2, 2.3

PREDICTION_PROBABILITY, 1.1.1, 1.1.2, 1.1.3.1, 2.3, 4.3, 4.3.1.5

PREDICTION_SET, 2.3, 4.3.1.6

predictive analytics, 1.3, 2.1.3

PREP_AUTO, 3.3.2

prior probabilities, 7.3.11

privileges, 3.5.2

PROFILE, 1.3, 2.1.3

R

regression, 3.2.2, 3.3.1

RegressionTestMetrics, 7.3.8

REMOVE_COST_MATRIX, 4.4

reverse transformations, 3.4, 5.2.4.1, 5.2.6, 5.2.6

rules, 4.3.1.4

S

sample programs, 3.5.3

scoring, 1.1.1, 2.1.1.2, 2.3, 4

batch, 4.5

data, 5.1.2

Java API, 7.3.9

real-time, 4.3

saving results, 4.3.4

scoring of attribute name, 5.2.5

settings table, 3.2, 7.3.2

sparse data, 5.4, 5.4

SQL AUDIT, 3.5

SQL COMMENT, 3.5

SQL data mining functions, 1, 2.3

STACK, 2.1.2, 3.3.2

supermodels, 5.1.2

supervised mining functions, 3.3.1

Support Vector Machines

See SVM

SVM, 3.2.2, 3.3.1, 3.3.1, 3.4, 7.3.14.2

SVM_CLASSIFIER index preference, 6.1, 6.4.1, 6.4.3

T

target, 5.2.2, 5.2.4.1

test data, 5.1.2

text mining, 6, 6, 6.2.1

text transformation, 6

Java, 6.1, 7.3.14.4

PL/SQL, 6.1

transactional data, 5.3.2, 5.3.2, 5.3.4, 5.3.4

transformation list, 3.3.2

transformations, 2.1.2, 3.3.2, 5, 5.2.4.1, 5.2.6, 5.2.6

transparency, 3.4, 5.2.6

U

unsupervised mining functions, 3.3.1