Index

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

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A

• accuracy 5.3.1.1, 5.3.2
• active sampling 23.1.2
• ADP
  • See: Automatic Data Preparation
• Aggregates
  • performance 11.4.7
• Algorithm
  • ESA 15.1.1, 15.1.2, 15.3
  • Explicit Semantic Analysis
    • Data Preparation
      • ESA 15.3
    • Scoring 15.1.1
  • Large ESA 15.1.2
  • Neural Network 21.4
    • Scoring 21.4
  • Random Forest 25.3
    • scoring 25.3
  • Scoring 15.1.1, 21.4, 25.3
    • Large ESA 15.1.2
• algorithms 3.2.1, 3.2.2
  • Apriori 3.2.2, 8.3, 11, 11.3.4, 11.3.4.1
  • association 11.1
  • Decision Tree 3.2.1, 13
  • defined 3.2
  • Expectation Maximization 3.2.2, 14
  • Exponential Smoothing 3.2.1, 10.4
  • Generalized Linear Model 17
  • Generalized Linear Models 3.2.1
  • k-Means 3.2.2, 7.3, 18
  • Minimum Description Length 3.2.1, 19
  • Naive Bayes 3.2.1, 20
  • Neural Network 3.2.1
    • regularization 21.1.5
  • Non-Negative Matrix Factorization 3.2.2, 22
  • O-Cluster 3.2.2, 7.3, 23
  • One-Class Support Vector Machine 3.2.2, 27.5
  • Principal Component Analysis 3.2.2, 26.1
  • Random Forest 3.2.1
  • Singular Value Decomposition 3.2.2, 26
  • supervised 3.2.1
  • Support Vector Machines 3.2.1
  • unsupervised 3.2.2
• Algorithms
  • About Algorithm Meta Data Registration 24.3
  • About ESA 15.1
  • About Exponential Smoothing 16.1
  • About Neural Network 21.1
  • About Random Forest 25.1
  • Accumulation 16.2.2
  • Algorithm Meta Data Registration 24, 24.3
  • Building a Random Forest 25.2
  • Column selection or attribute selection and row selection 12.4
  • CUR matrix decomposition 12, 12.3
  • CUR Matrix Decomposition 12.1, 12.2, 12.4
  • Data preparation 16.2
  • double exponential smoothing 16.1.3
  • ESA 15.1, 15.2
  • Explicit Semantic Analysis 15.1, 15.4
    • text mining 15.2
  • exponential smoothing 16.2.4
  • Exponential Smoothing 12.1, 12.2, 16, 16.1, 16.1.1, 16.1.2, 16.1.3, 16.1.4, 16.1.5, 16.1.6, 16.2, 16.2.1, 16.2.2, 16.2.3, 16.2.5
  • exponential smoothing models 16.2.4
  • Exponential Smoothing models 16.2.2
  • Exponential Smoothing Models 16.1.1, 16.2.1, 16.2.3, 16.2.5
  • Input Data 16.2.1
  • Missing value 16.2.3
  • Neural Network 21, 21.1
    • Convergence Check 21.1.6
    • Forward-backward propagation 21.1.3
    • LBFGS_SCALE_HESSIAN 21.1.7
    • Loss or Cost function 21.1.2
    • Neuron and activation function 21.1.1
    • NNET_HELDASIDE_MAX_FAIL 21.1.8
    • optimization solvers 21.1.4
  • Parallellish by partition 16.2.4, 16.2.5
  • Prediction intervals 16.1.6
  • Random Forest 25, 25.1, 25.2
  • Seasonality 16.1.4
  • Simple Exponential Smoothing 16.1.2
  • singular vectors 12.2
  • Statistical Leverage Score 12.3
  • Terminologies in Explicit Semantic Analysis 15.4
  • Text Mining 15.2
  • trend 16.1.3
  • Trend and Seasonality 16.1.5
• anomaly detection 3.1.2.1, 3.2.2, 5.3.2, 6, 7.1
• apply
  • See: scoring
• Apriori 3.2.2, 8.3, 11
  • aggregates 11.4.4
  • example: calculating aggregates 11.4.5
  • excluding rules 11.4.6
  • including rules 11.4.6
  • minimum support count 11.5.2
  • reverse confidence 11.5.4
• artificial intelligence 3.1
• association rules 3.1.2.1, 3.2.2, 8, 11
• attribute importance 3.1.1.2, 3.2.1, 3.2.2, 9, 19.1
  • CUR Matrix Decomposition 9.4
  • Minimum Description Length 9.4
• attributes 3.1.2.1
• Automatic Data Preparation 1.2.2, 1.3.2, 1.3.3, 3.3.1

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B

• Bayes Theorem 20.1
• binning 2.6, 22.3
  • equi-width 19.2, 23.3
  • supervised 19.2, 20.3

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C

• case table 1.3.2
• categorical target 5
• centroid 7.1.1, 18.1.2
• classification 3.1.1.2, 3.2.1, 5
  • biasing 5.3
  • binary 5.1, 17.8
  • Decision Tree 5.4, 13
  • default algorithm 5.4
  • Generalized Linear Model 17
  • Generalized Linear Models 5.4
  • logistic regression 17.8
  • multiclass 5.1
  • Naive Bayes 5.4, 20
  • one class 6.1.1
  • Support Vector Machine 27.4
  • Support Vector Machines 5.4
• class weights 5.3.2
• clustering 3.1.2.1, 3.2.2, 7
  • Expectation Maximization 14
  • hierarchical 3.2.2, 7.1.3, 7.2
  • k-Means 18.1
  • O-Cluster 23
  • scoring 3.1.2.1
• coefficients
  • GLM 17.2.1
  • Non-Negative Matrix Factorization 3.2.2, 22.1
  • regression 4.1.1, 4.1.1.3
• computational learning 1.1.6
• confidence 1.1.2
  • Apriori 3.2.2, 11.2.2
  • association rules 8.1.1, 11.5.3
  • clustering 7.1.3.2
  • defined 1.1.2
• confidence bounds 3.2.1, 4.1.1.6, 17.2.3
• confusion matrix 1.3.3, 5.2.1, 5.3.1.1
• cost matrix 5.3.1, 13.2.2
• costs 1.3.3, 5.3.1
• CUR Matrix Decomposition
  • configuration 12.5

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D

• data mining
  • defined 1.1
  • Oracle 2
• data mining techniques
  • association rules 8
  • attribute importance 9, 19.1
  • Build apply 10.3.4
  • clustering 7
  • time series 10
  • Time Series 10.3.4
• data preparation 1.2.2, 1.3.2
  • for Apriori 11.3
  • for Expectation Maximization 14.4
  • for Generalized Linear Model 17.6
  • for k-Means 18.3
  • for Minimum Description Length 19.2
  • for Naive Bayes 20.3
  • for Neural Network 21.2
  • for O-Cluster 23.3
  • for SVD 26.3
• data warehouse 1.1.7
• DBMS_DATA_MINING 2.5.1
• DBMS_STAT_FUNCS 2.6
• Decision Tree 3.2.1, 13
• deployment 1.3.4
• descriptive models 3.1.2
• directed learning 3.1.1

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E

• embedded data preparation 1.2.2
• entropy 13.2.1, 19.1.1, 19.1.1.3
• Exadata 2.3
• Expectation Maximization 3.2.2
• Explicit Semantic Analysis 3.2.1, 3.2.2
• Exponential Smoothing 3.2.1, 10.4

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F

• feature extraction 3.1.2.1, 3.2.2, 22, 26
• feature generation 17.3, 17.3.2
• feature selection 9, 17.3, 17.3.1
• frequent itemsets 11.1, 11.4.2
• Functions 24.1

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G

• Generalized Linear Model 17
  • classification 17.8.1
• Generalized Linear Models 3.2.1
  • feature selection and creation 17.3
  • regression 17.7
• GLM
  • See: Generalized Linear Model
  • Solvers 17.5
• graphical user interface 2.5.3

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H

• hierarchies 3.2.2, 7.1.3, 7.2

---

I

• inductive inference 1.1.6
• Interior Point Method 27.1.1
• itemsets 11.4

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K

• kernel 2.2
• k-Means 3.2.2, 7.3, 18

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L

• lift 1.3.3
  • association rules 11.5.5
  • classification 5.2.2
• linear regression 3.2.1, 4.1.1.1, 17.7
• logistic regression 3.2.1, 17.8

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M

• machine learning 3.1, 3.2.1
  • Oracle 3
  • process 1.3
• market basket data 1.3.3
• market-basket data 8.2
• MDL 3.2.1
  • See: Minimum Description Length
• Minimum Description Length 19
• mining functions 9
• Mining functions
  • Time Series
    • Statistics 10.3
• Mining Functions
  • Conditional Log-Likelihood 10.3.1
  • Time Series 10.3.1
• Mining Technique
  • classification 15.1
• mining techniques 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
  • attribute importance 3.2.1, 3.2.2
  • classification 3.1.1.2, 3.2.1, 5
  • clustering 3.1.2.1, 3.2.2
  • feature extraction 3.1.2.1, 3.2.2, 9.3
  • regression 3.1.1.2, 3.2.1, 4
  • time series 3.2.1
• Mining Techniques
  • Irregular Time Series 10.3.3
  • MSE 10.3.2
  • Prediction 10.3.2
  • Time Series 10.3.2, 10.3.3
• missing value treatment 3.3.1
• model details 1.3.4, 13.1.1
• multicollinearity 17.2.4
• multidimensional analysis 1.1.6, 2.6
• multivariate linear regression 4.1.1.2

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N

• Naive Bayes 3.2.1, 20
• nested data 11.3.1
• Neural Network 3.2.1
  • configuration 21.3
• NMF
  • See: Non-Negative Matrix Factorization
• nonlinear regression 4.1.1.4
• Non-Negative Matrix Factorization 3.2.2, 22
• nontransactional data 8.2
• numerical target 5.1

---

O

• O-Cluster 3.2.2, 7.3, 23
• OLAP 1.1.6, 2.6
• One-Class Support Vector Machine 3.2.2, 27.5
• optimization solvers 21.1.4
• Oracle Business Intelligence Suite Enterprise Edition 2.6
• Oracle Database
  • kernel 2.1
  • statistical functions 2.6
• Oracle Data Miner 2.5.3
• Oracle OLAP 2.6
• Oracle Spatial 2.6
• Oracle Text 2.6
• outliers 1.2.2, 6.1.3, 23.3.1
• overfitting 3.1.1.1, 13.2.3

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P

• parallel execution 3.4.1, 11.1, 13.1.2, 19.1, 20.1.1
• partitioned model 2.4
• PCA
  • See: Principal Component Analysis
• PL/SQL API 2.5, 2.5.1
• PREDICTION_PROBABILITY function 2.6
• PREDICTION Function 2.5.2
• predictive analytics 2.5.4
• predictive models 3.1.1
• Principal Component Analysis 3.2.2, 26.1
• prior probabilities 5.3.2, 20.1

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R

• Random Forest 3.2.1
• Receiver Operating Characteristic 5.2.3
• Registration 24.1
• 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 Model 17
  • Generalized Linear Models 4.3
  • linear 4.1.1.1, 17.7
  • nonlinear 4.1.1.4
  • ridge 17.2.4
  • statistics 4.2.1
  • Support Vector Machine 4.3, 27.6
• R extensibility 24.1
• R extensible
  • Build and score with R 24.2
• ridge regression 17.2.4
• ROC
  • See: Receiver Operating Characteristic
• R scripts registration 24.1
• rules 1.3.4
  • Apriori 11.1
  • association rules 8.1.1
  • clustering 7.1.3.1
  • Decision Tree 13.1.1
  • defined 1.1.2

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S

• sampling 11.3.4
• sampling implementation 11.3.4.1
• scoring 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 22.1.2
  • O-Cluster 23.1.4
  • parallel execution 3.4.1
  • real time 1.3.4
  • supervised models 3.1.1.2
  • unsupervised models 3.1.2.1
• singularity 17.2.4
• Singular Value Decomposition 26
• sparse data 3.3.1, 11.3
• SQL data mining functions 2.5, 2.5.2
• SQL statistical functions 2.6
• star schema 11.3.1
• statistical functions 2.6
• statistics 1.1.5
• stratified sampling 5.3.2, 6.1.1
• Sub-Gradient Descent 27.1.1
• supervised learning 3.1.1
• support 1.1.2
  • Apriori 3.2.2, 11.4.2
  • association rules 8.1.1, 11.5.1
  • clustering 7.1.3.2
  • defined 1.1.2
• Support Vector Machine 3.2.1, 27
  • classification 3.2.1, 27.4
  • Gaussian kernel 3.2.1
  • linear kernel 3.2.1
  • one class 3.2.2, 27.5
  • regression 3.2.1, 27.6
• SVD
  • See: Singular Value Decomposition
• SVM
  • See: Support Vector Machine

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T

• target 3.1.1, 3.1.2.1
• text analysis 22.1.3
• text mining 3.3.3
• time series 3.2.1
• transactional data 1.3.3, 8.2, 11.3
• transformations 3.3.1
• transparency 3.3.1, 7.2, 13.1.1, 17.2.1

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U

• unstructured data 3.3.3
• unsupervised learning 3.1.2
• UTL_NLA 2.6

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W

• wide data 9.1, 17.2.2