Index

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

A

accuracy, 3.5.2.1, 5.3.1.1, 5.3.2

active learning, 18.1.4

active sampling, 17.1.2

ADP

See Automatic Data Preparation

algorithms

Apriori, 2.4.2, 8.3, 10, 19.2.4

Decision Tree, 2.4.1, 11, 19.2.4

Generalized Linear Models, 2.4.1, 12, 19.2.4

k-Means, 2.4.2, 7.4, 13, 19.2.4

Minimum Description Length, 2.4.1, 14, 19.2.4

Naive Bayes, 2.4.1, 15, 19.2.4

Non-Negative Matrix Factorization, 2.4.2, 16, 19.2.4

O-Cluster, 2.4.2, 7.4, 17, 19.2.4

One-Class Support Vector Machine, 2.4.2, 18.5

supervised, 2.4.1, 2.4.1

Support Vector Machine, 2.4.1, 18, 19.2.4

unsupervised, 2.4.2

anomaly detection, 2.3.2.1, 2.3.2.1, 2.3.3, 2.4.2, 5.3.2, 6, 7.1

apply

See scoring

Apriori, 1.1.7, 2.4.2, 10, 19.2.4

area under the curve, 5.2.3.2

artificial intelligence, 2.3

association rules, 1.1.7, 2.3.2.1, 2.3.3, 2.4.2, 8, 10

attribute importance, 2.3.3, 2.4.1, 3.5.1, 9, 14.1

attributes, 1.3.1, 2.3.3

Automatic Data Preparation, 1.2.2, 1.3.3, 2.5.1, 19.1

B

Bayes' Theorem, 15.1

binning, Preface, 2.9, 16.3, 19.2.1

equi-width, 13.2, 14.2, 17.3, 19.3.2.1

quantile, 19.3.2.1

supervised, 14.2, 15.3, 19.3.2.1

top-N frequency, 19.3.2.1

C

case table, 1.1.7, 1.3.2, 19.1.1

categorical attributes, 3.5.2.1, 5, 19.1.2

centroid, 7.1.1, 13.1.2

classification, 2.3.3, 2.4.1, 5

biasing, 5.3

binary, 5.1, 12.5

Decision Tree, 11

Generalized Linear Models, 12

logistic regression, 12.5

multiclass, 5.1

Naive Bayes, 15

one class, 6.1.1

Support Vector Machine, 18.4

clipping, 19.2.3

clustering, 2.3.2.1, 2.3.2.1, 2.3.3, 2.4.2, 7

hierarchical, 2.4.2, 7.2, 7.3

K-Means, 13.1

O-Cluster, 17

scoring, 2.3.2.1, 2.3.2.1

coefficients

Non-Negative Matrix Factorization, 2.4.2, 16.1

regression, 4.1.1, 4.1.1.3

computational learning, 1.1.6

confidence

Apriori, 2.4.2, 10.2.2, 10.5.2

association rules, 8.1.1

clustering, 7.2.2

defined, 1.1.2

predictive analytics, 3.1

confidence bounds, 2.4.1, 4.1.1.6, 12.1.1.3

confusion matrix, 1.3.3, 5.2.1, 5.3.1.1

cost matrix, 5.3.1, 5.3.1.3, 11.2.2

costs, 1.3.3, 5.3.1, 5.3.1.3, 5.3.1.3

CREATE_MODEL, 2.7.2

cube, 1.1.6

D

data mining

automated, 3

defined, 1.1

Oracle, 2

process, 1.3

data preparation, 1.2.2, 1.3.2, 2.5, 19

for Apriori, 10.3, 19.2.4

for Decision Tree, 19.2.4

for Generalized Linear Models, 12.3, 19.2.4

for k-Means, 13.3, 19.2.2, 19.2.4

for Minimum Description Length, 14.2, 19.2.4

for Naive Bayes, 15.3, 19.2.4

for Non-Negative Matrix Factorization, 19.2.2, 19.2.4

for O-Cluster, 17.3, 19.2.4

for Support Vector Machine, 19.2.2, 19.2.4

data types, 19.1.2

data warehouse, 1.1.7

DBMS_DATA_MINING, 2.7.2

DBMS_DATA_MINING_TRANSFORM, 2.7.2, 2.9

DBMS_FREQUENT_ITEMSET, 2.9

DBMS_PREDICTIVE_ANALYTICS, 2.7.2, 3.3

DBMS_STAT_FUNCS, 2.9

Decision Tree, 2.4.1, 3.5.3, 3.5.3, 11, 19.2.4

deployment, 1.3.4

deprecated features, Preface

descriptive models, 2.3.2

dimensioned data, 2.5

directed learning, 2.3.1

discretization

See binning

DMSYS schema

See desupported features

documentation, 2.8

E

embedded data preparation, 1.2.2, 2.5.1, 19

entropy, 11.2.1, 14.1.1, 14.1.1.3

Exadata, 2.2

Excel, 3.2

EXPLAIN, 2.7.2, 2.7.6

F

feature extraction, 2.3.2.1, 2.3.3, 2.4.2, 9, 16, 20.2.6, 20.3

feature selection

See attribute importance

frequent itemsets, 10.1, 10.4.2

G

Generalized Linear Models, 2.4.1, 12, 19.2.4

classification, 12.5.1

regression, 12.4

gini, 11.2.1

GLM

See Generalized Linear Models

H

hierarchies, 1.1.6, 2.4.2, 7.2, 7.3

histogram, 13.2

I

inductive inference, 1.1.6

itemsets, 10.4

J

Java API, Preface, 2.1, 2.7.7, 2.7.7

K

KDD, 1.1, Glossary

kernel, 2.1

k-Means, 2.4.2, 7.4, 13, 19.2.4

L

lift, 1.3.3, 5.2.2, 5.2.2.1, 10.5.3

linear regression, 2.4.1, 4.1.1.1, 12.4

logistic regression, 2.4.1, 12.5

M

machine learning, 2.3

market basket data, Preface, 1.1.7, 1.3.3

MDL, 2.4.1, 3.5.1

See Minimum Description Length

Minimum Description Length, 14, 19.2.4

mining functions, 2.3, 2.3.3

anomaly detection, 2.3.3, 2.4.2, 6

association rules, 2.3.3, 2.4.2, 8

attribute importance, 2.4.1, 9, 14.1

classification, 2.3.3, 2.4.1, 5

clustering, 2.3.3, 2.4.2, 7

feature extraction, 2.3.3, 2.4.2, 9, 9.3

regression, 2.3.3, 2.4.1, 4

missing value treatment, Preface, 19.1

model details, 1.3.4, 3.5.1, 3.5.3, 11.1.1

multicollinearity, 12.1.2

multidimensional analysis, 1.1.6, 2.9, 2.9

multivariate linear regression, 4.1.1.2

N

Naive Bayes, 2.4.1, 15, 19.2.1, 19.2.4

nested data, Preface, 2.5, 10.3.1

neural networks, 18.1.1

NMF

See Non-Negative Matrix Factorization

nonlinear regression, 4.1.1.4

Non-Negative Matrix Factorization, 2.4.2, 16, 19.2.4

nontransactional data, 8.2

normalization, Preface, 19.2.2, 19.2.2

min-max, 19.3.2.2

scale, 19.3.2.2

z-score, 19.3.2.2

numerical attributes, 5.1, 19.1.2

O

O-Cluster, 2.4.2, 7.4, 17, 19.2.4

OLAP, 1.1.6, 2.9

One-Class Support Vector Machine, 2.4.2, 18.5

Oracle Business Intelligence Suite Enterprise Edition, 2.9

Oracle Data Miner, Preface, 20.3

Oracle Data Mining discussion forum, 2.8.1

Oracle Data Mining documentation, 2.8

Oracle Database analytics, 2.9

Oracle Database kernel, 2.1

Oracle Database statistical functions, 2.9

Oracle OLAP, 2.9

Oracle Spatial, 2.9

Oracle Spreadsheet Add-In for Predictive Analytics

See Spreadsheet Add-In

Oracle Text, 2.9, 2.9, 20.4

outliers, 1.2.2, 6.1.3, 17.3.1, 19.2.3, 19.3.2.3

overfitting, 2.3.1.1, 11.2.3

P

PL/SQL API, 2.7, 2.7.2

PMML, 2.7, 2.7.5

PREDICT, 2.7.2, 2.7.6

PREDICTION_PROBABILITY, 2.7.3

predictive analytics, 2.7.2, 2.7.6, 3

predictive confidence, 3.2, 3.4, 3.5.2.1

predictive models, 2.3.1

prior probabilities, 5.3.2, 15.1

probability threshold, 5.2.2.1, 5.2.3, 5.2.3.4

PROFILE, 2.7.2, 2.7.6

pruning, 11.2.3

R

R, 2.7

radial basis functions, 18.1.1

Receiver Operating Characteristic

See ROC

regression, 2.3.3, 2.4.1, 4

coefficients, 4.1.1, 4.1.1.3

defined, 4.1.1

Generalized Linear Models, 12

linear, 4.1.1.1, 12.4

nonlinear, 4.1.1.4

ridge, 12.1.2

statistics, 4.2.1

Support Vector Machine, 18.6

ROC, 3.5.2, 5.2.3, 5.2.3.2

rules, 1.3.4

Apriori, 10.1

association rules, 8.1.1

clustering, 7.2.1

Decision Tree, 3.5.3, 3.5.3, 3.5.3, 11.1.1

defined, 1.1.2

PROFILE, 3.5.3

S

sample programs, 2.8.1

scoring, 2.3.2.1, 2.3.2.1, 2.3.2.1

anomaly detection, 2.3.2.1

classification, 2.3.1.2

clustering, 2.3.2.1

defined, 1.1.1

Exadata, 2.2

knowledge deployment, 1.3.4

model details, 1.3.4

Non-Negative Matrix Factorization, 16.1.2

O-Cluster, 17.1.4

real time, 1.3.4

regression, 2.3.1.1

supervised models, 2.3.1.2

unsupervised models, 2.3.2.1

singularity, 12.1.2

sparse data, Preface, 10.3, 19.1

Spreadsheet Add-In, 2.7.6, 2.8.1, 3.2

SQL data mining functions, 2.7, 2.7.3

SQL statistical functions, 2.9

star schema, 2.5, 10.3.1

statistics, 1.1.5

stratified sampling, 5.3.2, 6.1.1

supermodel, 2.5.1, 2.5.1

supervised learning, 2.3.1

support

Apriori, 2.4.2, 10.4.2, 10.5.1

association rules, 8.1.1

clustering, 7.2.2

defined, 1.1.2

Support Vector Machine, 2.4.1, 3.5.2, 18, 19.2.4

classification, 2.4.1, 18.4

Gaussian kernel, 2.4.1

linear kernel, 2.4.1

one class, 2.4.2, 18.5

regression, 2.4.1, 18.6

SVM

See Support Vector Machine

T

target, 2.3.1, 2.3.2.1

term extraction, 20.3

text mining, 20

data types, 20.2.2

Non-Negative Matrix Factorization, 16.1.3

pre-processing, 20.2

transactional data, 1.1.7, 1.3.3, 8.2, 10.3

transformations, 2.5.1, 2.7.2, 19.1, 19.2.4

transparency, 7.3, 11.1.1, 12.1.1.1, 19.1

trimming, 19.3.2.3

U

unstructured data, 2.5, 20.1

unsupervised learning, 2.3.2

UTL_NLA, 2.9

W

wide data, 9.1, 12.1.1.2

windsorize, 19.3.2.3

X

XML

Decision Tree, 3.5.3, 11.1.3

PROFILE, 3.5.3