Skip Headers
Oracle® Data Mining Application Developer's Guide
11g Release 2 (11.2)

Part Number E12218-07
Go to Documentation Home
Home
Go to Book List
Book List
Go to Table of Contents
Contents
Go to Master Index
Master Index
Go to Feedback page
Contact Us

Go to previous page
Previous
PDF · Mobi · ePub

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
See also scoring
ApplySettings object, 2.4.3.6
Apriori, 5.2.2, 5.2.2
association rules, 5.2.2, 5.3.1, 5.4
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

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
See also apply
settings table, 2.4.3.2
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 Machine, 5.2.2, 5.3.1, 5.4
SVM
See Support Vector Machine
SVM_CLASSIFIER index preference, 4.1, 4.4.1, 4.4.3

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