Index

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

A

accuracy, 3.5.2.1, 5.3.1, 5.4.1.1, 5.4.2

active learning, 18.1.4

algorithms

Apriori, 2.3.2, 8.3, 10

Decision Tree, 2.3.1, 5.5, 11

Generalized Linear Model, 2.3.1, 4.4, 5.5, 12

k-Means, 2.3.2, 7.3, 13

Minimum Description Length, 2.3.1, 9.4, 14

Naive Bayes, 2.3.1, 5.5, 15

Non-Negative Matrix Factorization, 2.3.2, 9.4, 16, 16

O-Cluster, 2.3.2, 7.3, 17, 17.1

One-Class Support Vector Machine, 6.3

one-class Support Vector Machine, 2.3.2, 6.1.3, 18.5

supervised, 2.3.1, 2.3.1

Support Vector Machine, 2.3.1, 4.4, 5.5, 18

unsupervised, 2.3.2

ALTER_REVERSE_EXPRESSION, 19.4.2.1

anomaly detection, 2.2.2.1, 2.2.3, 2.3.2, 5.4.2, 6, 7.1

sample data, 6.2

sample problem, 6.2

antecedent, 8.1.3.1

API

See application programming interface

application programming interface, 2.1

apply, 2.2.1.2

Apriori, 2.3.2, 20.2.3

area under the curve, 5.3.4.2

artificial intelligence, 2.2

ASSO_MAX_RULE_LENGTH, 8.1.2

ASSO_MIN_CONFIDENCE, 8.1.3.2

ASSO_MIN_SUPPORT, 8.1.2.2

association

sample problem, 8.2

association rules, 2.2.2.1, 2.2.3, 2.3.2, 8.1

antecedent and consequent, 8.1.3.1

confidence, 8.1.4.2

data, 8.1.1

lift, 8.1.4.3

minimum confidence, 8.1.3.2, 8.1.3.2

minimum support, 8.1.2.2

support, 8.1.4.1

text mining, 20.2.7

attribute histogram, 7.1.1, 7.1.4

attribute importance, 2.2.1.2, 2.2.3, 2.3.1, 3.5.1, 9.2, 14.1

model details, 3.5.1

sample data, 9.2.1

sample problem, 9.2.2

attributes, 1.3.1, 2.2.3

finding the best, 9.1

Automatic Data Preparation, 1.2.2, 1.3.3, 1.3.3, 2.4.1, 19.1

B

Bayes' Theorem, 5.5, 15.1

benefits, 5.4.1.3

binary target, 5.1

binning, Preface

BLAS library, 2.7

blog, 2.6.1

C

case table, 1.1.7, 1.3.2, 19.1.1

categorical, 5.1

centroid, 7.1.1, 7.1.5

CLAS_COST_TABLE_NAME, 5.4.1.3

CLAS_PRIORS_TABLE_NAME, 5.4.2

CLAS_WEIGHTS_TABLE_NAME, 5.4.2

class weights, 5.4.2

classification, 2.2.3, 2.3.1, 5

biasing, 5.4

one class, 6.1.3

sample data, 5.2

sample problem, 5.2

testing, 5.3

text mining, 20.2.4

CLUS_NUM_CLUSTERS, 7.1.3.2

cluster details, 7.2.1

clustering, 2.2.2.1, 2.2.3, 2.3.2, 7, 13.1

hierarchical, 7.1.2

sample data, 7.2

sample problem, 7.2

scoring, 7.1.6, 7.2.2

text mining, 20.2.5

clusters

rules, 7.1.3

computational learning, 1.1.6

confidence, 7.1.3.1, 8.1.3.2, 8.1.4.2

defined, 1.1.2

confidence bounds, 12.1.1.3

confusion matrix, 1.3.3, 5.3.2, 5.4.1.1

consequent, 8.1.3.1

cost matrix, 5.4.1, 5.4.1.3

cost/benefit matrix, 11.1.3.2

costs, 1.3.3, 5.4.1, 5.4.1.3

Decision Tree, 5.4.1.3

counter-examples, 6.1

CREATE_MODEL, 2.5.1

cube, 1.1.6

cumulative gain, 5.3.3.1

cumulative lift, 5.3.3.1

cumulative number of nontargets, 5.3.3.1

cumulative number of targets, 5.3.3.1

cumulative percentage of records, 5.3.3.1

cumulative target density, 5.3.3.1

D

data

automatic preparation, 1.2.2, 1.3.3, 19

cleansing, 1.3.2

collection and exploration, 1.3.2

dimensioned, 1.1.6, 2.4

embedded preparation, 19

highly dimensioned, 9.1

market basket, Preface, 1.3.3

multi-record case, 8.1.1

nested, 8.1.1.2

one class, 6.1.1

single-record case, 1.1.7, 8.1.1

sparse, 8.1.1.2, 10.2

transactional, 8.1.1

unstructured, 20.1

wide, 9.1

data mining

automated, 3

defined, 1.1

Oracle, 2

process, 1.3

data preparation, 1.2.2, 1.3.2, 2.4, 19

automatic, 19

clustering, 7.1

embedded, 1.2.2, 19

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

DBMS_DATA_MINING_TRANSFORM, 2.5.1, 2.7

DBMS_FREQUENT_ITEMSET, 2.7

DBMS_FREQUENT_ITEMSETS, 8.1.2

DBMS_PREDICTIVE_ANALYTICS, 2.5.1, 3.3.1

DBMS_STAT_FUNCS, 2.7

Decision Tree, 2.3.1, 3.5.3, 5.5

model details, 3.5.3

rules, 3.5.3, 5.2

demo programs, 2.6.1

deployment, 1.3.4, 1.3.4

deprecated features, Preface

descriptive models, 2.2.2

directed learning, 2.2.1

distance-based clustering models, 13.1

DMSYS schema

See desupported features

documentation, 2.6

E

embedded data preparation, 2.4.1, 19.1

entropy, 11.1.3.1

Excel, 3.2

EXPLAIN, 2.5.1, 3.1.3, 9.2.3

attribute importance, 9.2.3

F

false negatives, 1.3.3, 5.3.4.4

false positive, 5.3.4.1, 5.4.1.2

false positive fraction, 5.3.4.4

false positives, 1.3.3, 5.3.4.4

FEAT_NUM_FEATURES, 9.3

feature extraction, 2.2.2.1, 2.2.3, 2.3.2, 9.3, 20.3

coefficients, 9.3.2

model details, 9.3.2

sample data, 9.3.1

sample problem, 9.3.2

scoring, 9.3.3

text mining, 20.2.6

features, 2.2.3, 9.3, 20.3

frequent itemsets, 8.1.2

G

Generalized Linear Models, 2.3.1, 4.4, 5.5, 9.1, 20.2.3

GET_MODEL_DETAILS, 19.4

GET_MODEL_TRANSFORMATIONS, 19.4.1

gini, 11.1.3.1

grid-based clustering models, 17.1

H

hierarchical clustering, 7.1.2

hierarchies, 1.1.6

clusters, 7.1.1

histogram, 7.1.1, 7.1.4

I

inductive inference, 1.1.6

itemsets, 8.1.2

J

Java API, 2.5.3, 3.3.2

K

KDD, 1.1

kernel, 2.1

k-Means, 2.3.2, 7.3, 13.1, 13.1, 13.1, 20.2.3

L

LAPACK library, 2.7

lift, 1.3.3, 5.3.3

association rules, 8.1.4.3

sample chart, 5.3.3

statistics, 5.3.3.1

linear regression, 2.3.1, 4.1.1.1, 4.4, 5.5

Logistic Regression, 20.2.4.3

logistic regression, 2.3.1

class weights, 5.4.2

M

machine learning, 2.2

market-basket

sample problem, 8.2

market-basket analysis, 8.1

market-basket data, 1.3.3

MDL

See Minimum Description Length

Mean Absolute Error, 4.3.2.2

medoid, 7.1.5

Minimum Description Length, 2.3.1, 3.5.1, 9.4, 14.1

Minimum Descriptor Length, 20.2.3

mining functions, 2.2, 2.2.3

anomaly detection, 2.2.3, 2.3.2, 6

association rules, 2.2.3, 2.3.2, 8

attribute importance, 2.2.3, 2.3.1, 9, 9.2, 14.1

classification, 2.2.3, 2.3.1, 5

clustering, 2.2.3, 2.3.2, 7

feature extraction, 2.2.3, 2.3.2, 9, 9.3

regression, 2.2.3, 2.3.1, 4

missing value treatment, Preface, 19.1

model details, 1.3.4, 3.5.1, 3.5.3, 11.1.1

models

anomaly detection, 2.2.3

association rules, 2.2.3

attribute importance, 2.2.3

classification, 5.1

clustering, 2.2.3, 7.1

deploying, 1.3.4

feature extraction, 2.2.3

over fitting, 11.1.3.3

regression, 2.2.3

supervised, 2.2.1, 2.3.1, 4, 5

unsupervised, 2.2.2

multiclass target, 5.1

multicollinearity, 12.1.2

multidimensional analysis, 2.7

multidimensional data, 1.1.6, 2.7

multiple regression, 4.1.1.2

multivariate linear regression, 4.1.1.2

N

Naive Bayes, 2.3.1, 5.4.2, 5.5, 20.2.3

prior probabilities, 5.4.2

negative class, 5.4.1.2

nested data, 2.4, 8.1.1.2

neural networks, 18.1.1

NMF

See Non-Negative Matrix Factorization

nonlinear regression, 4.1.1.4

Non-Negative Matrix Factorization, 2.3.2, 9.4, 20.2.3

data preparation, 16.2

normalization, Preface

numerical, 5.1

O

O-Cluster, 2.3.2, 7.3, 17.1

OLAP, 1.1.6, 1.1.6, 2.7

one-class classification, 6.1.3

One-Class SVM, 2.3.2, 6.3, 18.5

Oracle Data Miner, 4.2, 4.3.2.3, 5.2, 5.4.2, 6.2.1, 7.1.4, 9.2.2, 20.3

Oracle Data Mining discussion forum, 2.6.1

Oracle Database analytics, 2.7

Oracle Database kernel, 2.1

Oracle Database statistical functions, 2.7

Oracle Discoverer, 2.7

Oracle Intermedia, 2.7

Oracle OLAP, 2.7

Oracle Portal, 2.7

Oracle Spatial, 2.7

Oracle Spreadsheet Add-In for Predictive Analytics, 2.6.1, 3.2

Oracle Text, 2.7, 2.7, 20.5

Orthogonal Partitioning Clustering

See O-Cluster

outliers, 1.2.2, 6.1, 6.1.2

overfitting, 2.2.1.1

P

PL/SQL API, 2.5.1

positive class, 5.4.1.2

PREDICT, 2.5.1, 3.1.3

PREDICTION_PROBABILITY, 2.5.2, 6.2.2

predictive analytics, 2.5.4, 3, 9.2.3

accuracy, 3.4

Java API, 3.3.2

PL/SQL API, 3.3.1

Q

quantile lift, 5.3.3.1

R

radial basis functions, 18.1.1

Receiver Operating Characteristic, 3.5.2, 5.3.4

receiver operating characteristic

statistics, 5.3.4.2

regression, 2.2.3, 2.3.1, 4

defined, 4.1.1

sample build data, 4.2

sample problem, 4.2

testing, 4.1, 4.3

training, 4.1, 4.1

regression coefficients, 4.1.1, 4.1.1.3

regression parameters, 4.1.1

regularization, 18.1

residual, 4.1.1

residual plot, 4.3.1

reverse transformations, 19.4.2

ridge regression, 12.1.2

ROC

See receiver operating characteristic

Root Mean Squared Error, 4.3.2

rules, 1.3.4

association, 8.1.3

clusters, 7.1.1, 7.1.3

confidence, 1.1.2

Decision Tree, 3.5.3, 11.1.1

PROFILE, 3.5.3

support, 1.1.2

S

scoring, 1.3.4, 1.3.4, 2.2.1.2, 2.5.2

defined, 1.1.1

real time, 1.3.4

segment, 7.1

single-class data, 6.1.1

single-record case, 1.1.7

single-record case data, 8.1.1

singularity, 12.1.2

slope, 4.1.1.1

sparse data, 8.1.1.2, 10.2, 19.1

Spreadsheet Add-In

See Oracle Spreadsheet Add-In for Predictive Analytics

Spreadsheet Add-In for Predictive Analytics, 9.2.3

SQL data mining functions, 2.5.2, 2.5.2

star schema, 2.4

statistical functions, 2.7

statistics, 1.1.5

stratified sampling, 5.4.2

supermodel, 2.4.1, 2.4.1

supervised algorithms, 2.3.1

supervised learning, 2.2.1

support, 7.1.3.1, 8.1.2.2, 8.1.4.1

defined, 1.1.2

Support Vector Machine, 2.3.1, 3.5.2, 5.5, 9.1, 20.2.3

active learning, 18.1.4

classification, 2.3.1, 5.4.2

Gaussian kernel, 2.3.1

linear kernel, 2.3.1

one class, 2.3.2

regression, 2.3.1, 4.4

text mining, 20.2.4.3

SVM

class weights, 5.4.2

See Support Vector Machine

T

target, 2.2.1, 2.2.2.1

target density, 5.3.3.1

term extraction, 20.3

terms, 20.3

testing

classification model, 5.3

regression model, 4.1, 4.3

text

pre-processing, 20.3

text features, 20.3

text mining

algorithms, 20.2.3

association rules, 20.2.7

classification, 20.2.4

clustering, 20.2.5

data types, 20.2.2

feature extraction, 20.2.6

logistic regression, 20.2.4.3

pre-processing, 20.2

sample data, 20.4

sample problem, 20.4

Support Vector Machine, 20.2.4.3

text terms, 20.3

timestamp data, 19.1.2.1

training, 2.2.1

regression model, 4.1

transactional data, 1.3.3, 8.1.1

transformations, 2.4.1, 2.5.1, 19.1

transparency, 11.1.1, 12.1.1.1, 19.1, 19.3.2, 19.4

true negatives, 5.3.4.4

true positive, 5.3.4.1, 5.4.1.2

true positive fraction, 5.3.4.4

true positives, 5.3.4.4

U

unstructured data, 2.4, 20.1

unsupervised algorithms, 2.3.2

unsupervised learning, 2.2.2

UTL_NLA, 2.7

V

Vapnik's theory, 4.4

W

white papers, 2.6.1

wide data, 9.1

X

XML

Decision Tree, 3.5.3, 11.1.4

PROFILE, 3.5.3

Y

y intercept, 4.1.1.1