Index

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

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A

ABN See adaptive bayes network

active learning, 3.1.1.4.1

adaptive bayes network, 3.1.1.3

data preparation, 3.1.2

model types, 3.1.1.3.1

naive bayes build, 3.1.1.3.1

outliers, 3.1.2.1

rules, 3.1.1.3.2

single feature build, 3.1.1.3.1, 3.1.1.3.2

AI See attribute importance

algorithm, apriori, 4.2.3

algorithms

adaptive bayes network, 3.1.1.3.1

clustering, 4.1.1

decision tree, 3.1.1.1

feature extraction, 4.3.1

k-means, 4.1.1.1

naive bayes, 3.1.1.2

non-negative matrix factorization, 4.3.1

O-Cluster, 4.1.1.2

O-cluster, 4.1.1.2

regression, 3.2.1

anomaly detection

algorithm, 3.4.1

applying models, 3.1

apriori algorithm, 4.2.3

association models, 4.2

algorithm, 4.2.3

confidence, 4.2

data, 2.2.4

data preparation, 4.2.1

rare events, 4.2.2.1

sparse data, 4.2.1

support, 4.2

text mining, 6.2.4

association rules, 4.2

support and confidence, 4.2

attribute importance, 3.3

algorithm, 3.3.2.1

minimum descriptor length, 3.3.2.1

attributes, 2.1

categorical, 2.1

data type, 2.1

numerical, 2.1, 2.1

text, 2.1

unstructured, 2.1

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B

Bayes' Theorem, 3.1.1.2

benefits

in database data mining, 1.2

bin boundaries, 2.3.2.1

computing, 2.3.2.1

binning, 2.3.2

bin boundaries, 2.3.2.1

equi-width, 2.3.2.1

for O-cluster, 4.1.1.2.2

most frequent items, 2.3.2.1

bioinformatics, 8

BLAST, 8

example, 8.3

query example, 8.3

query results, 8.3

variants in ODM, 8.3

BLASTN, 8.3

BLASTP, 8.3

BLASTX, 8.3

boosted model, 3.1.1.3.1

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C

cases, 2.1

categorical attributes, 2.1

centroid, 4.1

classification

costs, 3.1.3

data preparation, 3.1.2

outliers, 3.1.2.1

text mining, 6.2.1

use, 3.1

classification models, 3.1

building, 3.1

testing, 3.1, 3.1

clipping, 2.3.1, 2.3.1

cluster centroid, 4.1

clustering, 4.1, 4.1.1.1

algorithms, 4.1.1

k-means, 4.1.1.1

O-cluster, 4.1.1.2

orthogonal partitioning, 4.1.1.2

text mining, 6.2.2

column data types, 2.2.2

columns

nested, 2.2.2.1

data types, 2.2.2.1

confidence

of association rule, 4.2

confusion matrix, 3.5.1

figure, 3.5.1

cost matrix, 3.1.3

costs, 3.1.3

of incorrect decision, 3.1

CRISP_DM

ODM support for, 5.1

CRISP-DM, 5.1

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D

data

evaluation, 3.1

for ODM, 2

format, 2.2.1

model building, 3.1

preparation, 2.3

prepared, 2.3

requirements, 2.2

sparse, 2.2.4, 4.2.1

table format, 2.2.1

test, 3.1

training, 3.1

unstructured, 2.1

data mining, 1.1

in database, 1.2

benefits, 1.2, 1.2

methodology, 5.1

ODM, 1.3

Oracle, 1.3

steps, 5.1

unsupervised, 4

data mining automation, 5.2.2

data mining, supervised, 3

data preparation, 2.3

association models, 4.2.1

binning, 2.3.2

classification, 3.1.2

clustering, 4.1

discretization, 2.3.2

k-means, 4.1.1.1.1

normalization, 2.3.3

support vector machine, 3.1.1.4.4

data requirements, 2.2

data table format, 2.2.1

data types

columns, 2.2.2

DBMS_DATA_MINING

confusion matrix, 3.5.1

lift, 3.5.2

DBMS_PREDICTIVE_ANALYTICS package, 5.2.2

decision tree, 3.1.1.1

algorithm, 3.1.1.1

PMML, 3.1.1.1.2

rules, 3.1.1.1.1

XML, 3.1.1.1.2

discretization, 2.3.2

See binning

distance-based clustering models, 4.1.1.1

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E

equi-width binning, 2.3.2.1

export

models, 5.2.5

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F

feature, 4.3

feature extraction, 4.3

Oracle Text, 6.2.3

text, 6.2.3, 6.2.3

text mining, 4.3.1.1

fixed collection types, 2.2.2.1

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G

grid-based clustering models, 4.1.1.2

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I

import

models, 5.2.5

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K

k-means, 4.1.1.1

cluster information, 4.1.1.1

compared with O-cluster, 4.1.1.4

data preparation, 4.1.1.1.1

hierarchical build, 4.1.1.1

scoring, 4.1.1.1.3

unbalanced approach, 4.1.1.1

k-means algorithm, 4.1.1.1

k-means and O-cluster comparison (table), 4.1.1.4

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L

lift, 3.5.2

statistics, 3.5.2

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M

market basket analysis, 4.2

MDL See minimum descriptor length

minimum descriptor length, 3.3.2.1

missing values, 2.2.3

handling, 2.2.3.2

mixture model, 4.1.1.1.3

model deployment, 5.2.5

models

apply, 3.1

association, 4.2

building, 3.1

classification, 3.1

clustering, 4.1

deployment, 5.2.5

export, 5.2.5, 7.4

import, 5.2.5, 7.4

moving, 7.4

supervised, 3

training, 3.1

unsupervised, 4

most frequent items, 2.3.2.1

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N

naive bayes

data preparation, 3.1.2

outliers, 3.1.2.1

naive bayes algorithm, 3.1.1.2

NB See naive bayes

nested columns, 2.2.2.1

network feature, 3.1.1.3.1

NMF See non-negative matrix factorization

non-negative matrix factorization, 4.3.1

data preparation, 4.3.1.2

paper, 4.3.1

text, 6.2.3

text mining, 4.3.1.1

normalization, 2.3.3

null

values support vector machine, 3.1.2.2

null vales

adaptive bayes network, 3.1.2.2

null values, 2.2.3.1

classification, 3.1.2.2

decision tree, 3.1.2.2

naive bayes, 3.1.2.2

numerical data type, 2.1

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O

O-cluster

apply, 4.1.1.2.4

attribute types, 4.1.1.2.3

binning, 4.1.1.2.2

compared with k-means, 4.1.1.4

data preparation, 4.1.1.2.2

scoring, 4.1.1.2.4

O-cluster algorithm, 4.1.1.2

ODM, 1.3

attributes, 2.1

graphical interfaces, 5.2.4

programming interfaces, 5.2.1

scoring engine, 7

ODM interfaces, 5.2

one-class

text mining, 6.2.6

one-class support vector machine, 3.4.1

how to specify, 3.4.1.1

one-class SVM, 3.4.1

Oracle data miner, 5.2.4

Oracle data mining, 1.3

data, 2

Oracle Text, 6

orthogonal partitioning clustering, 4.1.1.2

outlier treatment, 2.3.1

outliers, 2.2.5

treatment, 2.3.1

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P

PMML

decision tree, 3.1.1.1.2

predictive analytics

add-in, 5.2.4

preparation

data, 4.1

prepared data, 2.3

priors, 3.1.4

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R

rare events

association models, 4.2.2.1

receiver operating characteristics, 3.5.3

figure, 3.5.3

statistics, 3.5.3

regression, 3.2

text mining, 6.2.5

ROC See receiver operating characteristics

rules

adaptive bayes network, 3.1.1.3.2

association model, 4.2

decision tree, 3.1.1.1.1

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S

scoring, 3.1, 4.1.1.1.3

in applications, 7.3

O-cluster, 4.1.1.2.4

scoring data, 3.1

scoring engine, 7

application deployment, 7.5

features, 7.1

installation, 7.2

use, 7.5

sequence alignment, 8

ODM capabilities, 8.3

sequence search, 8

ODM capabilities, 8.3

settings

support vector machine, 3.1.1.4.4

single feature build, 3.1.1.3.1

sparse data, 2.2.4, 4.2.1

association models, 4.2.1

supervised data mining, 3

support

of association rule, 4.2

support vector machine

active learning, 3.1.1.4.1

algorithm, 3.1.1.4

classification, 3.1.1.4

text, 6.2.1

data preparation, 3.1.1.4.4, 3.1.2

one class, 6.2.6

one-class, 3.4.1

outliers, 3.1.2.1

regression, 3.2.1

text, 6.2.5

settings, 3.1.1.4.4

text mining, 6.2.6

weights, 3.1.3

SVM See support vector machine

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T

TBLASTN, 8.3

TBLASTX, 8.3

testing models, 3.1

text features, 6.1

text mining, 4.3.1.1, 6

association models, 6.2.4

classification, 6.2.1

clustering, 6.2.2

feature extraction, 4.3.1.1, 6.2.3

non-negative matrix factorization, 4.3.1.1

ODM support, 6

Oracle support, 6.3

regression, 6.2.5

support (table), 6.3

support vector machine, 6.2.1

tree rules, 3.1.1.1.1

trimming, 2.3.1, 2.3.1

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U

unstructured attributes, 2.1

unstructured data, 2.1

text, 6

unsupervised data mining, 4

unsupervised models, 4

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W

weights, 3.1.3

winsorizing, 2.3.1

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X

XML

decision tree, 3.1.1.1.2