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, 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
- 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
- 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, 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 object, 2.4.3.4
- 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, 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, 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, 2.4.3.7
- transparency, 3.2.6, 5.4
U
- unsupervised mining functions, 5.3.1