Index

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

A

ABN, 3-4

Adaptive Bayes Network, 3-4

model states, 3-8

model types, 3-5

rules, 3-5

single feature build, 3-5

AI, 3-10

algorithms

Adaptive Bayes Network, 3-5

apriori, 4-10

clustering, 4-2

feature extraction, 4-11

k-means, 4-2

Naive Bayes, 3-3

non-negative matrix factorization, 4-11

O-Cluster, 4-5

O-cluster, 4-5

regression, 3-10

settings for, 5-2

settings for (Java), 6-2

support vector machine, 3-9

apply result object, 6-5

applying models, 3-2

association

text mining, 8-4

association models, 4-7

algorithm, 4-10

apriori algorithm, 4-10

confidence, 4-8

data, 2-8, 4-9

dense data, 4-9

rare events, 4-8

support, 4-8

association rules

support and confidence, 4-8

Attribute Importance, 3-10

attribute importance, 3-10

algorithm, 3-11

attribute importance minimum descriptor length, 3-11

attribute names and case, 6-4

attribute types, 2-6

attributes, 2-1

categorical, 2-2

data type, 2-1

numerical, 2-2

target, 2-7

text, 2-2

unstructured, 2-2

B

balanced approach

k-means, 4-3

Bayes' Theorem, 3-3

best model

model seeker, 3-12

bin boundaries, 2-11

computing, 2-11

binning, 2-10

bin boundaries, 2-11

equi-width, 2-11

for k-means, 4-4

for O-Cluster, 4-6

most frequent items, 2-11

bioinformatics, 10-1

BLAST, 10-1

example, 10-3

query example, 10-3

query results, 10-4

variants in ODM, 10-2

BLASTN, 10-2

BLASTP, 10-2

BLASTX, 10-2

build parameters

in ABN, 3-6

build result object, 6-5

C

cases, 2-1

categorical attributes, 2-2

centroid, 4-2

classification, 3-1

testing models, 3-2

text mining, 8-3

use, 3-2

classification models

building, 3-1

evaluation, 7-6

testing, 3-2

cluster centroid, 4-2

clustering, 4-1, 4-2

k-means, 4-2

O-cluster, 4-5

orthogonal partitioning, 4-5

text mining, 8-3

column data types, 2-5

CompleteMultiFeature

ABN model state, 3-8

CompleteSingleFeature

ABN model state, 3-8

confidence

of association rule, 4-8

confusion matrix, 6-5, 7-7

figure, 6-6, 7-7

continuous data type, 4-6

cost matrix, 3-2

table, 7-5

costs, 3-2, 7-5

of incorrect decision, 3-2

cross-validation, 3-4

D

data

association models, 4-9

evaluation, 3-2

model building, 3-1

preparation, 2-10

prepared, 2-10

requirements, 2-2

single-record case, 2-2

sparse, 2-8, 4-9

table format, 2-2

test, 3-2

training, 3-1

unprepared, 2-10

wide, 2-3

data format

figure, 2-4

data mining, 1-1

ODM, 1-1

Oracle, 1-1

data mining server, 5-2, 6-2

data mining tasks per function, 5-1

data preparation, 2-10

binning, 2-10

DBMS_DATA_MINING, 2-10

discretization, 2-10

normalization, 2-12

support vector machine, 3-9

data preprocessing

clustering, 4-1

data requirements, 2-2

data storage, 2-7

data table format, 2-2

multi-record case, 2-2

single-record case, 2-2

wide data, 2-3

data types

attribute type, 2-6

columns, 2-5

data usage specification object, 6-4

date data type, 2-5

dates in ODM, 2-5

DBMS_DAT_MINING

cost matrix

table, 7-5

DBMS_DATA_MINING

algorithms, 7-3

application development, 7-1

apply results, 7-6

build results, 7-6

classification model evaluation, 7-6

confusion matrix, 7-7

costs, 7-5

export models, 7-11

functions, 7-3

import models, 7-11

lift, 7-8

mining function, 7-2

model build, 7-2

models, 7-2

prior probabilities, 7-4

priors, 7-4

regression model test, 7-10

settings tables, 7-3

dense data

association models, 4-9

descriptive models, 1-2

discretization, 2-10

distance-based clustering models, 4-2

DMS, 6-2

DUS, 6-4

E

equi-width binning, 2-11

F

feature comparison (table), A-2

feature extraction, 4-10

text mining, 4-11, 8-4

figure of merit, 3-13

fixed collection types, 2-4

function settings, 5-2

G

grid-based clustering models, 4-5

I

IncompleteSingleFeature

ABN model state, 3-8

incremental approach

in k-means, 4-3

input

to apply phase, 6-6

J

Java interface, 5-1

K

k-means, 4-2

balanced approach, 4-3

cluster information, 4-3

compared with O-cluster, 4-7

hierarchical build, 4-3

scoring, 4-5

unbalanced approach, 4-3

version comparison (table), 4-4

k-means algorithm, 4-2

data, 4-4

k-means and O-Cluster (table), 4-7

L

LDS, 6-3, 6-4

lift, 5-3, 7-8

lift result object, 6-5

logical data specification object, 6-3, 6-4

M

market basket analysis, 4-7

MaximumNetworkFeatureDepth, ABN parameter, 3-6

MDL, 3-11

MFS, 6-1

minimum descriptor length, 3-11

mining algorithm settings object, 6-2

mining apply output object, 6-6

mining attribute, 6-3

mining function

DBMS_DATA_MINING, 7-2

mining function settings object, 6-1

mining model

export, 7-10

import, 7-10

mining model object, 6-4

mining models

export, 9-2

import, 9-2

moving, 9-2

mining result object, 6-5

missing values, 2-7

handling, 2-7

mixture model, 4-5

model apply

Java interface, 5-3

Java interface (figure), 5-4

model building

Java interface, 5-2

model building (figure), 5-3

model export

Java interface, 5-5

native, 9-2

PMML, 5-6, 9-2

model import

Java interface, 5-5

PMML, 5-6, 9-2

model seeker, 3-12

model states, 3-8

CompleteMultiFeature, 3-8

CompleteSingleFeature, 3-8

IncompleteSingleFfature, 3-8

NaiveBayes, 3-8

model testing

java interface, 5-3

lift, 5-3

models

apply, 3-2

association, 4-7

building, 3-1

classification, 3-1

clustering, 4-1

DBMS_DATA_MINING, 7-2

descriptive, 1-2, 4-1

export, 7-10

import, 7-10

predictive, 1-2, 3-1

training, 3-1

most frequent items, 2-11

multi-record case, 2-3

multi-record case data table format, 2-2

Java interface, 2-3

views, 2-4

N

Naive Bayes algorithm, 3-3

NavieBayes

ABN model state, 3-8

NB, 3-3

nested table format, 2-3

NMF, 4-11

non-negative matrix factorization, 4-11

nontransactional, 6-1

see single-record case, 2-2

normalization, 2-12

null values, 2-7

numerical data type, 2-2, 4-2, 4-6

O

O-cluster

apply, 4-6

attribute types, 4-6

binning, 4-6

compared with k-means, 4-7

data preparation, 4-6

scoring, 4-6

O-Cluster algorithm, 4-5

ODM, 1-1

ODM features, A-2

ODM programming environments, A-4

Oracle Data Mining

scoring engine, 9-1

Oracle data mining, 1-1

data, 2-1

orthogonal partitioning clustering, 4-5

outliers, 2-8

P

PDS, 6-1

physical data specification, 6-1

PL/SQL interface

algorithms, 7-3

functions, 7-3

PMML, 5-6

export, 9-2

import, 9-2

Java interface, 5-6

Predictive Model Markup Language, 5-6

predictive models, 1-2, 3-1

prepared data, 2-10

preprocessing

data, 4-1

prior probabilities, 7-4

priors, 3-3, 7-4

programming environments, A-4

R

rare events

association models, 4-8

receiver operating characteristics, 7-8

figure, 7-9

statistics, 7-10

regression, 3-10

algorithm, 3-10

text mining, 8-4

regression models

test, 7-10

ROC, 7-8

rules

Adaptive Bayes Network, 3-5

S

scoring, 3-2, 4-5

in applications, 9-1

O-Cluster, 4-6

scoring data, 3-2, 9-1

scoring engine, 9-1

application deployment, 9-3

features, 9-1

installation, 9-1

use, 9-3

sequence alignment, 10-1

ODM capabilities, 10-2

sequence search, 10-1

ODM capabilities, 10-2

setting tables, 7-3

settings

support vector machine, 3-9

single-record case, 2-3

single-record case format, 2-2

sparse data, 2-8, 4-9

summarization, 4-7

in k-means, 4-5

support

of association rule, 4-8

support vector machine, 3-9

data preparation, 3-9

regression, 3-10

settings, 3-9

SVM, 3-9

T

targets, 2-7

TBLASTN, 10-2

TBLASTX, 10-2

test result object, 6-5

testing models, 3-2

DBMS_DATA_MINING, 7-6

text mining, 4-11, 8-1

association, 8-4

classification, 8-3

clustering, 8-3

feature extraction, 4-11, 8-4

ODM support, 8-1

regression, 8-4

support (figure), 8-5

transactional, 6-1

see multi-record case, 2-3

U

unbalanced approach

k-means, 4-3

unprepared data, 2-10

unstructured attributes, 2-2

unstructured data, 2-5

W

wide data, 2-3

fixed collection types, 2-4

nested table format, 2-3

winsorizing, 2-11

figure, 2-12