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Oracle Data Mining Concepts
10g Release 1 (10.1)

Part Number B10698-01
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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