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Oracle® Data Mining Concepts
10g Release 2 (10.2)

Part Number B14339-01
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Index

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

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

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

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

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

E

equi-width binning, 2.3.2.1
export
models, 5.2.5

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

G

grid-based clustering models, 4.1.1.2

I

import
models, 5.2.5

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

L

lift, 3.5.2
statistics, 3.5.2

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

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

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

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

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

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

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

U

unstructured attributes, 2.1
unstructured data, 2.1
text, 6
unsupervised data mining, 4
unsupervised models, 4

W

weights, 3.1.3
winsorizing, 2.3.1

X

XML
decision tree, 3.1.1.1.2