Oracle9i Data Mining Concepts -- Index

Adaptive Bayes Network

sample programs, A-2

Adaptive Bayes Network (ABN), 1-2, 1-10

algorithms, 1-9

settings for, 1-19

API

ODM, 2-1

apply result object, 1-26

ApplyContentItem, 3-11

Apriori algorithm, 1-4, 1-18

Association Rules, 1-2, 1-4, 1-7

sample programs, A-4

support and confidence, 1-8

Attribute Importance, 1-2, 1-4, 1-8, 1-17

sample programs, A-4

using, 2-4

attribute names and case, 1-26

attributes

find, 2-4

use, 2-4

automated binning (see also discretization), 1-2

balance

in data sample, 1-5

Bayes' Theorem, 1-12, 1-13

best model

find, 2-3

in Model Seeker, 1-14

binning, 1-29

automated, 1-30

for k-means, 1-15

for O-Cluster, 1-16

manual, 1-30

sample programs, A-5

build data

describe, 3-3

build model, 3-6

build result object, 1-26

categorical data type, 1-2

character sets

CLASSPATH, 2-2

classifcation

specifying Naive Bayes, 3-5

classification, 1-4

sample program, A-2

specifying default algorithm, 3-5

CLASSPATH for ODM, 2-1

clustering, 1-2, 1-4, 1-6, 1-15

sample programs, A-3

compiling sample programs, A-5

Complete single feature, ABN parameter, 1-12

computing Lift, 1-21

confidence

of associatioin rule, 1-8

confusion matrix, 1-26, 1-27

figure, 1-27

costs

of incorrect decision, 1-5

cross-validation, 1-13

data

scoring, 3-7

data format

figure, 1-24

data mining API, 1-3

data mining components, 1-3

data mining functions, 1-4

data mining server

connect to, 3-3, 3-9

data mining server (DMS), 1-3, 1-19, 1-24

data mining tasks, 1-19

data mining tasks per function, 1-19

data preprocessing, 1-6

data scoring

main steps, 3-8

output data, 3-10

prerequisites, 3-8

data types, 1-2

data usage specification (DUS) object, 1-25

decision tree models

sample programs, A-2

decision trees, 1-2, 1-10

discretization (binning), 1-29

sample programs, A-5

distance-based clustering model, 1-15

DMS

connect to, 3-3, 3-9

enhanced k-means algorithm, 1-15

executing sample programs, A-5

feature

definition, 1-11

feature selection, 1-2

features

new, 1-2

function settings, 1-19

functions

data mining, 1-4

grid-based clustering model, 1-16

incremental approach

in k-means, 1-15

input

to apply phase, 1-28

input columns

including in mining apply output, 3-12

input data

data scoring, 3-9

describe, 3-9

jar files

ODM, 2-1

Java Data Mining (JDM), 1-3

Java Specification Request (JSR-73), 1-3

key fields, 1-2

k-means, 1-2

k-means algorithm, 1-4, 1-15

binning for, 1-15

k-means and O-Cluster (table), 1-17

learning

supervised, 1-2, 1-4

unsupervised, 1-2, 1-4

leave-one-out cross-validation, 1-13

lift result object, 1-26

location access data

apply output, 3-10

build, 3-3

data scoring, 3-9

logical data specification (LDS) object, 1-25

market basket analysis, 1-7

max build parameters

in ABN, 1-10

MaximumNetworkFeatureDepth, ABN parameter, 1-10

metadata repository, 1-3

MFS, 3-4

validate, 3-6

mining algorithm settings object, 1-25

mining apply

output data, 3-10

mining apply output, 1-27

mining attribute, 1-25

mining function settings

build, 3-4

creating, 3-4

validate, 3-6

mining function settings (MFS) object, 1-24

mining model object, 1-26

mining result object, 1-26

mining tasks, 1-3

MiningApplyOutput object, 3-10

MiningFunctionSettings object, 3-4

missing values, 1-29

model

apply, 3-1

build

synchronous, 3-6

building, 3-1

score, 3-1

model apply, 3-7, 3-13

ApplyContentItem, 3-11

ApplyMutipleScoringItem, 3-11

ApplyTargetProbabilityItem, 3-11

asynchronous, 3-14

generated columns in output, 3-11

including input columns in output, 3-12

input data, 3-9

main steps, 3-8

physical data specification, 3-9

specify output format, 3-10

synchronous, 3-13

validate output object, 3-13

model apply (figure), 1-22

model apply (scoring), 1-22

model build

asynchronous, 3-7

model building, 1-19

main steps, 3-2

outline, 2-2

overview, 3-2

prerequisites, 3-2

model building (figure), 1-20

Model Seeker, 1-2, 1-14

sample programs, A-3

using, 2-3

model testing, 1-21

multi-record case (transactional format), 1-23

Naive Bayes, 1-2

algorithm, 1-12

building models, 3-1

sample programs, A-2

specifying, 3-5

nontransactional data format, 1-23

numerical data type, 1-2, 1-15, 1-16

O-Cluster, 1-2

algorithm, 1-16

sample programs, A-3

ODM

basic usage, 3-1

ODM algorithms, 1-9

ODM API, 2-1

ODM functionality, 1-23

ODM functions, 1-4

ODM jar files, 2-1

ODM models

building, 3-1

ODM objects, 1-23

ODM programming

basic usage, 3-1

overview, 2-1

ODM programs

compiling, 2-1

executing, 2-1

ODM sample programs, A-1

ODMprogramming

common tasks, 2-2

Oracle9i Data Mining API, 1-3

physical data specification

build

nontransactional, 3-4

transactional, 3-4

data scoring, 3-9

model apply, 3-9

nontransactional, 3-9

transactional, 3-9

physical data specification (PDS), 1-23

PhysicalDataSpecification, 3-9

PMML

sample programs, A-4

PMML export

sample program, A-4

PMML import

sample program, A-4

Predictive Model Markup Language (PMML), 1-2, 1-3, 1-31

predictor attribute, 1-4

Predictor Variance algorithm, 1-17

preprocessing

data, 1-6

priors information, 1-5

rules

decision tree, 1-10

sample programs, A-1

Adaptive Bayes Network, A-2

Association Rules, A-4

Attribute Importance, A-4

basic usage, A-2

binning, A-5

classification, 3-5, A-2

compiling and executing, A-5, A-7

decision tree models, A-2

discretization, A-5

Model Seeker, A-3

Naive Bayes, A-2

O-Cluster, A-3

PMML export, A-4

PMML import, A-4

short, 3-1

short programs, A-2

scoring, 1-5, 1-16, 1-22

by O-Cluster, 1-17

output data, 3-10

prerequisites, 3-8

scoring data, 3-7

sequence of ODM tasks, 2-3

short sample programs, A-2

compiling and executing, A-5

single-record case (nontransactional format), 1-24

skewed data sample, 1-5

SQL/MM for Data Mining, 1-3

summarization

in k-means, 1-15

supervised learning, 1-2, 1-4

support

of association rule, 1-8

target attribute, 1-4

test result object, 1-26

transactional data format, 1-23

unsupervised learning, 1-2, 1-4

unsupervised model, 1-14

Index

A

B

C

D

E

F

G

I

J

K

L

M

N

O

P

R

S

T

U