Index

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

A

accuracy, 5.3.1.1, 5.3.2

active learning, 20.1.4

active sampling, 18.1.2

ADP

See Automatic Data Preparation

algorithms

Apriori, 3.2.2, 8.3, 10

association, 10.1

Decision Tree, 3.2.1, 11

defined, 3.2

Expectation Maximization, 3.2.2, 12

Generalized Linear Models, 3.2.1, 13

k-Means, 3.2.2, 7.3, 14

Minimum Description Length, 3.2.1, 15

Naive Bayes, 3.2.1, 16

Non-Negative Matrix Factorization, 3.2.2, 17

O-Cluster, 3.2.2, 7.3, 18

One-Class Support Vector Machine, 3.2.2, 20.5

Principal Component Analysis, 3.2.2, 19.1

Singular Value Decomposition, 3.2.2, 19

supervised, 3.2.1, 3.2.1

Support Vector Machines, 3.2.1

unsupervised, 3.2.2

anomaly detection, 3.1.2.1, 3.1.2.1, 3.1.2.1, 3.2.2, 5.3.2, 6, 7.1

apply

See scoring

Apriori, 3.2.2, 8.3, 10

artificial intelligence, 3.1

association rules, 3.1.2.1, 3.1.2.1, 3.2.2, 8, 10

attribute importance, 3.1.1.2, 3.2.1, 9, 15.1

Minimum Description Length, 9.4

B

Bayes Theorem, 16.1

binning, 2.5, 17.3

equi-width, 14.2, 15.2, 18.3

supervised, 15.2, 16.3

C

case table, 1.3.2

categorical target, 5

centroid, 7.1.1, 14.1.2

class weights, 5.3.2

classification, 3.1.1.2, 3.2.1, 5

biasing, 5.3

binary, 5.1, 13.7

Decision Tree, 5.4, 11

default algorithm, 5.4

Generalized Linear Models, 5.4, 13

logistic regression, 13.7

multiclass, 5.1

Naive Bayes, 5.4, 16

one class, 6.1.1

Support Vector Machines, 5.4, 20.4

clustering, 3.1.2.1, 3.1.2.1, 3.2.2, 7

Expectation Maximization, 12

hierarchical, 3.2.2, 7.1.3, 7.2

K-Means, 14.1

O-Cluster, 18

scoring, 3.1.2.1, 3.1.2.1

coefficients

GLM, 13.2.1

Non-Negative Matrix Factorization, 3.2.2, 17.1

regression, 4.1.1, 4.1.1.3

computational learning, 1.1.6

confidence

Apriori, 3.2.2, 10.2.2

association rules, 8.1.1, 10.5.2

clustering, 7.1.3.2

defined, 1.1.2

confidence bounds, 3.2.1, 4.1.1.6, 13.2.3

confusion matrix, 1.3.3, 5.2.1, 5.3.1.1

cost matrix, 5.3.1, 11.2.2

costs, 1.3.3, 5.3.1

D

data mining

defined, 1.1

Oracle, 2, 3

process, 1.3

data preparation, 1.2.2, 1.3.2

for Apriori, 10.3

for Expectation Maximization, 12.4

for Generalized Linear Models, 13.5

for k-Means, 14.3

for Minimum Description Length, 15.2

for Naive Bayes, 16.3

for O-Cluster, 18.3

for SVD, 19.3

data warehouse, 1.1.7

DBMS_DATA_MINING, 2.4.1

DBMS_DATA_MINING_TRANSFORM, 2.5, 3.3.1

DBMS_PREDICTIVE_ANALYTICS, 2.4.4

DBMS_STAT_FUNCS, 2.5

Decision Tree, 3.2.1, 11

deployment, 1.3.4

descriptive models, 3.1.2

directed learning, 3.1.1

E

embedded data preparation, 1.2.2

entropy, 11.2.1, 15.1.1, 15.1.1.3

Exadata, 2.3

Expectation Maximization, 3.2.2

F

feature creation, Preface

feature extraction, 3.1.2.1, 3.1.2.1, 3.2.2, 9, 17, 19, 19

default algorithm, 9.4

Non-Negative Matrix Factorization, 9.4

Principal Component Analysis, 9.4

Singular Value Decomposition, 9.4

feature generation, 13.3, 13.3.2

feature selection, Preface, 9, 13.3, 13.3.1

G

Generalized Linear Models, 3.2.1, 13

classification, 13.7.1

feature selection and creation, Preface, 13.3

regression, 13.6

GLM

See Generalized Linear Models

graphical user interface, 2.4.3

H

hierarchies, 3.2.2, 7.1.3, 7.2

histogram, 14.2

I

inductive inference, 1.1.6

itemsets, 10.4

K

kernel, 2.2

k-Means, 3.2.2, 7.3, 14

L

lift, 1.3.3

association rules, 10.5.3

classification, 5.2.2

linear regression, 3.2.1, 4.1.1.1, 13.6

logistic regression, 3.2.1, 13.7

M

machine learning, 3.1

market basket data, 1.3.3

MDL, 3.2.1

See Minimum Description Length

Minimum Description Length, 15

mining functions, 3.1, 3.1.1.2

anomaly detection, 3.1.2.1, 3.2.2, 6

association rules, 3.1.2.1, 3.2.2, 8

attribute importance, 3.2.1, 9, 15.1

classification, 3.1.1.2, 3.2.1, 5

clustering, 3.1.2.1, 3.2.2, 7

feature extraction, 3.1.2.1, 3.2.2, 9, 9.3

regression, 3.1.1.2, 3.2.1, 4

missing value treatment, 3.3.1

model details, 1.3.4, 11.1.1

multicollinearity, 13.2.4

multidimensional analysis, 1.1.6, 2.5, 2.5

multivariate linear regression, 4.1.1.2

N

Naive Bayes, 3.2.1, 16

nested data, 10.3.1

neural networks, 20.1.1

NMF

See Non-Negative Matrix Factorization

nonlinear regression, 4.1.1.4

Non-Negative Matrix Factorization, 3.2.2, 17

nontransactional data, 8.2

numerical target, 5.1

O

O-Cluster, 3.2.2, 7.3, 18

OLAP, 1.1.6, 2.5

One-Class Support Vector Machine, 3.2.2, 20.5

Oracle Business Intelligence Suite Enterprise Edition, 2.5

Oracle Data Miner, 2.4.3

Oracle Database

kernel, 2.1

statistical functions, 2.5

Oracle OLAP, 2.5

Oracle Spatial, 2.5

Oracle Text, 2.5, 2.5

outliers, 1.2.2, 6.1.3, 18.3.1

overfitting, 3.1.1.1, 11.2.3

P

parallel execution, 3.4.1, 10.1, 11.1.2, 12.2.1, 15.1, 16.1.1

PCA

See Principal Component Analysis

PL/SQL API, 2.4, 2.4.1

PREDICTION Function, 2.4.2

PREDICTION_PROBABILITY function, 2.5

predictive analytics, 2.4.4

predictive models, 3.1.1

Principal Component Analysis, 3.2.2, 19.1

R

radial basis functions, 20.1.1

Receiver Operating Characteristic, 5.2.3

regression, 3.1.1.2, 3.2.1, 4

coefficients, 4.1.1, 4.1.1.3

default algorithm, 4.3

defined, 4.1.1

Generalized Linear Models, 4.3, 13

linear, 4.1.1.1, 13.6

nonlinear, 4.1.1.4

ridge, 13.2.4

statistics, 4.2.1

Support Vector Machine, 4.3, 20.6

ridge regression, 13.2.4

ROC

See Receiver Operating Characteristic

rules, 1.3.4

Apriori, 10.1

association rules, 8.1.1

clustering, 7.1.3.1

Decision Tree, 11.1.1

defined, 1.1.2

S

scoring, 3.1.2.1, 3.1.2.1, 3.1.2.1

anomaly detection, 3.1.2.1

classification, 3.1.1.2

clustering, 3.1.2.1

defined, 1.1.1

dynamic, 3.4.2

Exadata, 2.3

knowledge deployment, 1.3.4

model details, 1.3.4

Non-Negative Matrix Factorization, 17.1.2

O-Cluster, 18.1.4

parallel execution, 3.4.1

real time, 1.3.4

regression, 3.1.1.1

supervised models, 3.1.1.2

unsupervised models, 3.1.2.1

Singular Value Decomposition, 19

T

target, 3.1.1, 3.1.2.1

text mining, 3.3.3, 17.1.3

transactional data, 1.3.3, 8.2, 10.3

transformations, 3.3.1

transparency, 3.3.1, 7.2, 11.1.1, 13.2.1

U

unstructured data, 3.3.3

unsupervised learning, 3.1.2

UTL_NLA, 2.5

V

Variance Inflation Factor, 13.2.4.3

W

wide data, 9.1, 13.2.2