Index

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

A

accuracy, 3.5.2.1

active learning, 18.1.4

See attribute importance, 9.2

algorithms

Apriori, 10, 10.1, 10.1

Decision Tree, 11

Generalized Linear Models, 12

k-Means, 13

Minimum Description Length, 14

Naive Bayes, 15

Non-Negative Matrix Factorization, 16, 16.1

O-Cluster, 17, 17.1

supervised, 2.3.1

Support Vector Machines, 18

unsupervised, 2.3.2

ALTER_REVERSE_EXPRESSION, 19.4.2.1

anomaly detection, 2.2.3, 6

Apriori, 2.3.2

association models, 8.1

algorithm, 10.1

confidence, 8.1

data preparation, 8.3

rare events, 8.1.1.1

sparse data, 8.3

support, 8.1

text mining, 20.3.4

association rules

See association models

attribute importance, 2.2.1.2, 2.2.3, 9.2

Automatic Data Preparation, 1.3.3, 2.4.1, 19.1

B

Bayes' Theorem, 5.2, 15.1

binary target, 5.1.1

binning, Preface

C

case table, 1.1.7, 19.1.1

centroid, 7.1

classification, 2.2.3, 5

costs, 5.3.1

one class, 6.2

text mining, 20.3.1

clustering, 2.2.3, 7.1, 13.1

text mining, 20.3.2

confidence, 1.1.2, 1.1.2, 8.1

confidence bounds, 12.1.1.3

confusion matrix, 1.3.3, 5.4.1

cost/benefit matrix, 5.3.1, 11.1.3.2

costs, 5.3.1

counter-examples, 6.1

CREATE_MODEL, 2.5.1

cube, 1.1.6

D

data

dimensioned, 1.1.6, 2.4

market basket, Preface, 1.3.3

single record case, 1.1.7

sparse, 8.3, 8.3

unstructured, 2.4

data mining process, 1.3

data preparation, 1.2.2, 1.3.2, 2.4, 2.4, 19

association models, 8.3

clustering, 7.1

missing values, 19.1

data types, 19.1.2

data warehouse, 1.1.7

date data, 19.1.2.1

DBMS_DATA_MINING, 2.5.1, 2.5.1

DBMS_DATA_MINING_TRANSFORM, 2.5.1, 2.5.1, 2.7

DBMS_FREQUENT_ITEMSET, 2.7

DBMS_PREDICTIVE_ANALYTICS, 2.5.1, 2.5.1, 3.3.1

DBMS_STAT_FUNCS, 2.7

Decision Tree, 2.3.1, 5.2, 5.3.2

demo programs, 2.6.1

deployment, 1.3.4

deprecated features, Preface

distance-based clustering models, 13.1

DMSYS schema

See deprecated features, Preface

documentation, 2.6

E

embedded data preparation, 2.4.1, 19.1

equal-width binning, 8.3

Excel, 3.2

EXPLAIN, 2.5.1, 3.1.3, 3.1.3, 3.2, 3.3.1, 3.5.1, 3.5.1

F

feature, 9.1

feature extraction, 2.2.3, 9.1

Oracle Text, 20.3.3

text, 20.3.3, 20.3.3

text mining, 16.2

G

generalization to new data, 18.1, 18.1, 18.1, 18.1

Generalized Linear Models, 2.3.1, 4.2, 5.2

GET_MODEL_DETAILS, 19.4

GET_MODEL_TRANSFORMATIONS, 19.4.1

grid-based clustering models, 17.1

J

Java API, 2.5.3, 3.3.2

K

KDD, 1.1

kernel, 2.1

k-Means, 2.3.2, 7.2, 13.1, 13.1, 13.1.1

Knowledge-Discovery in Databases

See KDD

L

lift, 1.3.3, 5.4.2

linear regression, 4.1.2.1, 4.2

logistic regression, 5.2, 5.3.2

M

market basket analysis, 8.1

market-basket data, 1.3.3, 1.3.3

MDL

See Minimum Description Length

Mean Absolute Error, 4.3.2

Minimum Description Length, 2.3.1, 14.1

mining functions, 2.2.3

missing value treatment, Preface, 19.1

mixture model, 13.1.1

models

association, 8.1

classification, 5.1

clustering, 7.1

supervised, 4, 5

unsupervised, 7

multiclass target, 5.1.1

multicollinearity, 12.1.2

multidimensional data, 1.1.6, 2.7

multiple regression, 4.1.2.3

multivariate regression, 4.1.2.3

N

Naive Bayes, 2.3.1, 5.2

nested data, 2.4

neural networks, 18.1.1

NMF

See Non-Negative Matrix Factorization

nonlinear regression, 4.1.2.2

Non-Negative Matrix Factorization, 2.3.2

data preparation, 16.3

text, 20.3.3

text mining, 16.2

normalization, Preface

O

O-Cluster, 2.3.2, 7.2, 17.2

OLAP, 1.1.6, 1.1.6

one-class classification, 6.2

One-Class SVM, 2.3.2, 6.3, 18.5

Oracle Data Mining discussion forum, 2.6.1

Oracle Database analytics, 2.7

Oracle Database statistical functions, 2.7

Oracle OLAP, 2.7

Oracle Spatial, 2.7

Oracle Spreadsheet Add-In for Predictive Analytics, 3.2

Oracle Text, 2.7, 2.7

Orthogonal Partitioning Clustering

See O-Cluster

outliers, 6.1, 8.3

P

PL/SQL API, 2.5.1

PREDICT, 2.5.1, 3.1.3, 3.1.3, 3.2, 3.2, 3.3.1, 3.4, 3.4, 3.5.2, 3.5.2

PREDICTION_PROBABILITY, 2.5.2

predictive analytics, 3, 3

Java API, 3.3.2

PL/SQL API, 3.3.1

R

radial basis functions, 18.1.1

rare events

association models, 8.1.1.1

Receiver Operating Characteristic, 5.4.3

regression, 2.2.3, 4

text mining, 20.3.5

regression coefficients, 4.1.2.1

regularization, 18.1, 18.1

reverse transformations, 19.4.2

ridge regression, 12.1.2

ROC, 5.4.3

Root Mean Squared Error, 4.3

rules

association model, 8.1

decision trees, 11.1.1

PROFILE, 3.5.3

S

scoring, 1.1.1, 1.1.1, 1.3.4, 13.1.1

O-Cluster, 17.2

single-record case data, 1.1.7

singularity, 12.1.2

slope, 4.1.2.1

sparse data, 8.3, 8.3, 19.1

Spreadsheet Add-In, 3.2

SQL data mining functions, 2.5.2

star schema, 2.4

statistical functions, 2.7

statistics, 1.1.5, 1.1.5

supermodel, 2.4.1

support, 1.1.2, 1.1.2, 8.1

Support Vector Machine, 2.3.1, 5.2

active learning, 18.1.4

classification, 5.3.2

text, 20.3.1

One-Class, 20.3.6

regression, 4.2

text, 20.3.5

text mining, 20.3.6

T

text features, 20.2

text mining, 16.2

association models, 20.3.4

classification, 20.3.1

clustering, 20.3.2

feature extraction, 16.2, 20.3.3

Non-Negative Matrix Factorization, 16.2

Oracle support, 20.4

regression, 20.3.5

support (table), 20.4

Support Vector Machine, 20.3.1

timestamp data, 19.1.2.1

transactional data, 1.3.3

transformations, 2.4.1, 2.5.1, 19.1

transparency, 11.1.1, 12.1.1.1, 19.1, 19.3.2, 19.4

U

unstructured data, 2.4

unsupervised models, 7

UTL_NLA, 2.7

V

Vapnik's theory, 4.2

W

white papers, 2.6.1

wide data, 4.2

X

XML

Decision Tree, 11.1.4

PROFILE, 3.5.3

Y

y intercept, 4.1.2.1