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Index
A
B
C
D
E
F
G
I
J
K
M
N
O
P
R
S
T
U
A
ABN,
3.3
,
3.3
,
6.2
settings,
3.3
test metrics,
1.3
Adaptive Bayes Network,
1.2
see also
ABN
steps in model development,
1.3
algo_name setting,
3.3
algorithm settings
Adaptive Bayes Network,
3.3
Decision Tree,
3.3
k
-Means,
3.3
Naive Bayes,
3.3
,
3.3
Non-Negative Matrix Factorization,
3.3
O-Cluster,
3.3
One-Class SVM,
3.3
Support Vector Machine,
3.3
anomaly detection,
1.1
,
1.2
,
1.3
apply,
1.3
,
1.8
,
7.11
apply results,
4.2.2
,
7.11
ApplySettings object,
6.3.6
,
7.11
Apriori,
1.2
,
1.3
,
3.3
steps in model development,
1.3
asso_max_rule_length setting,
3.3
asso_min_confidence setting,
3.3
asso_min_support setting,
3.3
association rules,
1.2
,
1.3
,
2.1.2
,
3.3
,
3.3
,
3.3
model details,
1.6
testing,
1.3
attribute importance,
1.2
,
1.3
,
3.3
model details,
1.6
testing,
1.3
attribute names,
2.2.2
attributes,
1.4
,
2.2
B
binning,
1.4
,
6.2
,
7.15.1
BLAST
NCBI,
9.1
ODM,
9.2
output,
9.2.4
sample data,
9.2.6
BLAST table functions
summary of,
9.2.6.6
BLASTN_ALIGN table function,
9.2.3
,
9.2.6.6
BLASTN_MATCH table function,
9.2.1
,
9.2.6.6
BLASTP_ALIGN table function,
9.2.6.6
BLASTP_MATCH table function,
9.2.2
,
9.2.6.6
build results,
4.2.1
BuildSettings object,
6.3.2
,
7.6
BuildTask object,
7.9
C
case ID,
1.4
,
2.3
Java API,
2.2
PL/SQL API,
2.2
SQL scoring functions,
2.2
categorical attributes,
1.4
,
2.2.1
clas_cost_table_name setting,
3.3
clas_priors_table_name setting,
3.3
classification,
1.2
,
1.3
,
3.3
model details,
1.6
scoring,
1.3
test metrics,
6.3.5
testing,
1.3
ClassificationTestMetrics,
7.10
CLASSPATH,
7.2
clipping,
1.4
,
6.2
,
7.15.3
clus_num_clusters setting,
3.3
CLUSTER_ID,
1.8
CLUSTER_PROBABILITY,
1.8
CLUSTER_SET,
1.8
clustering,
1.2
,
1.3
,
3.3
,
3.3
,
3.3
,
3.3
model details,
1.6
scoring,
1.3
testing,
1.3
collection types,
1.4
,
2.1.1
,
5.3
Connection object,
6.3
,
7.3
ConnectionFactory,
7.3.1
cost matrix table,
3.3
,
3.3.1
,
4.3.2
,
7.12
CTXSYS.DRVODM,
5.1
D
data
Java API,
7.4
,
7.5
non-transactional,
2.3
PhysicalDataSet,
6.3.1
preparation,
1.3
,
1.4
,
1.4
,
1.4
,
2
,
7.15
storage optimization,
2.4
transactional,
2.3
data storage,
2.4
data types,
2.1
,
2.2.1.1
DBMS_DATA_MINING,
4.2
DBMS_DATA_MINING_TRANSFORM,
1.4
DBMS_PREDICTIVE_ANALYTICS,
1.7
DBMS_SCHEDULER,
6.3.3
,
7.7
DBMS_STATS,
7.5
Decision Tree,
1.1
,
1.2
,
2.2
,
3.3
,
3.3
,
3.3.1
applying a model,
4.5
building a model,
4.3
details,
1.6
settings,
3.3
steps in model development,
1.3
test metrics,
1.3
testing a model,
4.4
DM_NESTED_CATEGORICALS,
1.4
,
2.1.1
,
2.3
DM_NESTED_NUMERICALS,
1.4
,
2.1.1
,
2.3
,
5.3
,
5.4.6
DM_USER_MODELS view,
3.1
,
4.2
DMS connection,
7.3.2
dmsh.sql,
5.2
dmtxtfe.sql,
5.2
DNA sequences,
9.2.1
E
EXPLAIN,
1.7
export,
3.2
F
feat_num_features setting,
3.3
feature extraction,
1.2
,
1.3
,
3.3
,
3.3
,
5.1
scoring,
1.3
testing,
1.3
FEATURE_EXPLAIN table function,
5.1
,
5.4.1
,
5.4.5.1
FEATURE_ID,
1.8
FEATURE_PREP table function,
5.1
,
5.4.1
,
5.4.4.1
FEATURE_SET,
1.8
FEATURE_VALUE,
1.8
function settings
summary of,
3.3
G
genetic codes,
9.2.6.4
I
import,
3.2
index preference,
5.1
J
Java API,
6
converting to,
8
data,
7.5
data transformations,
7.15
design overview,
7.4
interoperable with PL/SQL API,
1.1
,
8.1
mining tasks,
7.7
sample applications,
7.1
setting up the development environment,
7.2
text transformation,
7.15.4
using,
7
JDBC,
7.3.2
JDM standard,
6
named objects,
7.4
Oracle extensions,
6.2
,
6.2
K
k
-Means,
1.2
,
1.3
,
2.1.2
,
3.3
,
3.3
,
7.15.2
settings,
3.3
steps in model development,
1.3
M
matching
sequences,
9
MDL,
3.3
steps in model development,
1.3
mean absolute error,
4.2.4.2
Minimum Descriptor Length,
1.2
,
1.3
see also
MDL
mining
apply,
1.3
descriptive,
1.2
functions,
1.2
,
3.3
models,
3.1
new features,
1.1
operations,
4.2
predictive,
1.2
scoring,
1.3
steps,
1.3
supervised learning,
1.2
testing,
1.3
text,
5.2.1
,
7.1
unsupervised learning,
1.2
model details,
1.6
,
7.9
Model object,
6.3.4
models
accessing,
3.1.2
building,
1.3
,
1.5
,
7.8
function,
3.3
importing and exporting,
3.2
in Database,
3.1
metadata,
3.1
naming,
3.1.1
scoring,
1.3
,
7.11
settings,
3.3
,
3.3
,
4.3.2
,
7.6
settings table,
1.5
testing,
1.3
,
7.10
multi-record case,
2.3
N
Naive Bayes,
1.2
,
3.3
settings,
3.3
steps in model development,
1.3
test metrics,
1.3
NCBI,
9.1
nested tables,
1.4
,
2.1.1
,
2.1.1
,
2.3
,
5.3
,
5.4.6
,
7.15.4
NMF,
2.1.2
,
3.3
,
3.3
,
5.1
,
6.2
,
7.15.2
settings,
3.3
steps in model development,
1.3
Non-Negative Matrix Factorization,
1.2
,
1.3
see also
NMF
normalization,
1.4
,
6.2
,
7.15.2
numerical attributes,
1.4
,
2.2.1
O
O-Cluster,
1.2
,
3.3
,
3.3
,
6.2
settings,
3.3
steps in model development,
1.3
ODM BLAST,
9.2
One-Class SVM,
1.1
,
1.2
,
1.4
,
2.2
,
3.3
,
3.3
,
7.1
steps in model development,
1.3
OraBinningTransformation,
7.15.1
Oracle Spreadsheet Add-In for Predictive Analytics,
1.7
Oracle Text,
2.1.2
,
5
OraClippingTransformation,
7.15.3
OraExplainTask,
6.2
,
7.14
OraNormalizeTransformation,
7.15.2
OraPredictTask,
6.2
,
7.14
OraTextTransform,
2.1.2
OraTextTransformation,
7.15.4
outliers,
3.3
output of BLAST query,
9.2.4
P
persistentObject,
6.3
PhysicalDataSet,
6.3.1
PL/SQL API,
4
sample applications,
4.1
,
4.1
,
4.2
PMML,
1.6
PREDICT,
1.7
PREDICTION,
1.8
,
4.4
,
4.5
PREDICTION_COST,
1.8
,
4.5
PREDICTION_DETAILS,
1.8
,
4.5
PREDICTION_PROBABILITY,
1.8
PREDICTION_SET,
1.8
,
4.5
predictive analytics,
1.1
DATE and TIMESTAMP,
2.2.1.2
Java API,
6
,
7.14
Oracle Spreadsheet Add-In,
1.7
PL/SQL API,
1.7
prior probabilities,
7.13
prior probabilities table,
3.3
,
3.3.2
protein sequences,
9.2.2
R
records,
1.4
regression,
1.2
,
1.3
,
3.3
model details,
1.6
scoring,
1.3
test metrics,
6.3.5
testing,
1.3
RegressionTestMetrics,
7.10
root mean square error,
4.2.4.1
S
sample applications
Java,
7.1
PL/SQL,
4.1
,
4.2
term extraction for text mining,
5.2
scoring,
1.3
Java API,
7.11
PL/SQL API,
4.2.2
SQL functions,
1.8
,
1.8
,
4.4
sequence matching,
9
sequences
DNA,
9.2.1
protein,
9.2.2
settings,
3.3
settings table,
1.5
,
3.3
,
4.3.2
,
7.6
single-record case,
2.3
SQL scoring functions,
2.2
supervised learning,
1.2
,
1.4
Support Vector Machine,
1.2
,
1.2
see also
SVM
SVM,
1.3
,
2.1.2
,
3.3
,
3.3
,
3.3
,
3.3
,
7.15.2
SVM Classification,
3.3.2
steps in model development,
1.3
test metrics,
1.3
SVM Regression,
2.2
steps in model development,
1.3
,
1.3
test metrics,
1.3
,
4.2.4
SVM_CLASSIFIER index preference,
5.1
,
5.4.1
,
5.4.3
T
target column,
1.4
,
2.2
Task object,
6.3.3
TBLAST_ALIGN table function,
9.2.6.6
TBLAST_MATCH table function,
9.2.6.6
,
9.2.6.6
term extraction,
5.1
,
5.4
test results,
4.2.3
testing,
1.3
,
7.10
classification models,
4.2.3
,
7.10
regression models,
4.2.4
,
7.10
TestMetrics object,
6.3.5
text mining,
1.4
,
2.1.2
,
5
sample Java applications,
7.1
sample PL/SQL applications,
5.2.1
text transformation,
1.4
,
2.1.2
,
5
,
6.2
Java,
5.1
,
7.15.4
Java example,
7.15.4
PL/SQL,
5.1
PL/SQL example,
5.5
transientObject,
6.3
U
unsupervised learning,
1.2
,
1.4
user views,
3.1
,
4.2
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