Index

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

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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

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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

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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

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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

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E

EXPLAIN, 1.7

export, 3.2

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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

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G

genetic codes, 9.2.6.4

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I

import, 3.2

index preference, 5.1

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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

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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

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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

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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

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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

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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

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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

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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

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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

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U

unsupervised learning, 1.2, 1.4

user views, 3.1, 4.2