Index
A B C D E F G I J K L M N O P R S T U W
A
- ABN, 3-4
- Adaptive Bayes Network, 3-4
- model states, 3-8
- model types, 3-5
- rules, 3-5
- single feature build, 3-5
- AI, 3-10
- algorithms
- Adaptive Bayes Network, 3-5
- apriori, 4-10
- clustering, 4-2
- feature extraction, 4-11
- k-means, 4-2
- Naive Bayes, 3-3
- non-negative matrix factorization, 4-11
- O-Cluster, 4-5
- O-cluster, 4-5
- regression, 3-10
- settings for, 5-2
- settings for (Java), 6-2
- support vector machine, 3-9
- apply result object, 6-5
- applying models, 3-2
- association
- text mining, 8-4
- association models, 4-7
- algorithm, 4-10
- apriori algorithm, 4-10
- confidence, 4-8
- data, 2-8, 4-9
- dense data, 4-9
- rare events, 4-8
- support, 4-8
- association rules
- support and confidence, 4-8
- Attribute Importance, 3-10
- attribute importance, 3-10
- algorithm, 3-11
- attribute importance minimum descriptor length, 3-11
- attribute names and case, 6-4
- attribute types, 2-6
- attributes, 2-1
- categorical, 2-2
- data type, 2-1
- numerical, 2-2
- target, 2-7
- text, 2-2
- unstructured, 2-2
B
- balanced approach
- k-means, 4-3
- Bayes' Theorem, 3-3
- best model
- model seeker, 3-12
- bin boundaries, 2-11
- computing, 2-11
- binning, 2-10
- bin boundaries, 2-11
- equi-width, 2-11
- for k-means, 4-4
- for O-Cluster, 4-6
- most frequent items, 2-11
- bioinformatics, 10-1
- BLAST, 10-1
- example, 10-3
- query example, 10-3
- query results, 10-4
- variants in ODM, 10-2
- BLASTN, 10-2
- BLASTP, 10-2
- BLASTX, 10-2
- build parameters
- in ABN, 3-6
- build result object, 6-5
C
- cases, 2-1
- categorical attributes, 2-2
- centroid, 4-2
- classification, 3-1
- testing models, 3-2
- text mining, 8-3
- use, 3-2
- classification models
- building, 3-1
- evaluation, 7-6
- testing, 3-2
- cluster centroid, 4-2
- clustering, 4-1, 4-2
- k-means, 4-2
- O-cluster, 4-5
- orthogonal partitioning, 4-5
- text mining, 8-3
- column data types, 2-5
- CompleteMultiFeature
- ABN model state, 3-8
- CompleteSingleFeature
- ABN model state, 3-8
- confidence
- of association rule, 4-8
- confusion matrix, 6-5, 7-7
- figure, 6-6, 7-7
- continuous data type, 4-6
- cost matrix, 3-2
- table, 7-5
- costs, 3-2, 7-5
- of incorrect decision, 3-2
- cross-validation, 3-4
D
- data
- association models, 4-9
- evaluation, 3-2
- model building, 3-1
- preparation, 2-10
- prepared, 2-10
- requirements, 2-2
- single-record case, 2-2
- sparse, 2-8, 4-9
- table format, 2-2
- test, 3-2
- training, 3-1
- unprepared, 2-10
- wide, 2-3
- data format
- figure, 2-4
- data mining, 1-1
- ODM, 1-1
- Oracle, 1-1
- data mining server, 5-2, 6-2
- data mining tasks per function, 5-1
- data preparation, 2-10
- binning, 2-10
- DBMS_DATA_MINING, 2-10
- discretization, 2-10
- normalization, 2-12
- support vector machine, 3-9
- data preprocessing
- clustering, 4-1
- data requirements, 2-2
- data storage, 2-7
- data table format, 2-2
- multi-record case, 2-2
- single-record case, 2-2
- wide data, 2-3
- data types
- attribute type, 2-6
- columns, 2-5
- data usage specification object, 6-4
- date data type, 2-5
- dates in ODM, 2-5
- DBMS_DAT_MINING
- cost matrix
- table, 7-5
- DBMS_DATA_MINING
- algorithms, 7-3
- application development, 7-1
- apply results, 7-6
- build results, 7-6
- classification model evaluation, 7-6
- confusion matrix, 7-7
- costs, 7-5
- export models, 7-11
- functions, 7-3
- import models, 7-11
- lift, 7-8
- mining function, 7-2
- model build, 7-2
- models, 7-2
- prior probabilities, 7-4
- priors, 7-4
- regression model test, 7-10
- settings tables, 7-3
- dense data
- association models, 4-9
- descriptive models, 1-2
- discretization, 2-10
- distance-based clustering models, 4-2
- DMS, 6-2
- DUS, 6-4
E
- equi-width binning, 2-11
F
- feature comparison (table), A-2
- feature extraction, 4-10
- text mining, 4-11, 8-4
- figure of merit, 3-13
- fixed collection types, 2-4
- function settings, 5-2
G
- grid-based clustering models, 4-5
I
- IncompleteSingleFeature
- ABN model state, 3-8
- incremental approach
- in k-means, 4-3
- input
- to apply phase, 6-6
J
- Java interface, 5-1
K
- k-means, 4-2
- balanced approach, 4-3
- cluster information, 4-3
- compared with O-cluster, 4-7
- hierarchical build, 4-3
- scoring, 4-5
- unbalanced approach, 4-3
- version comparison (table), 4-4
- k-means algorithm, 4-2
- data, 4-4
- k-means and O-Cluster (table), 4-7
L
- LDS, 6-3, 6-4
- lift, 5-3, 7-8
- lift result object, 6-5
- logical data specification object, 6-3, 6-4
M
- market basket analysis, 4-7
- MaximumNetworkFeatureDepth, ABN parameter, 3-6
- MDL, 3-11
- MFS, 6-1
- minimum descriptor length, 3-11
- mining algorithm settings object, 6-2
- mining apply output object, 6-6
- mining attribute, 6-3
- mining function
- DBMS_DATA_MINING, 7-2
- mining function settings object, 6-1
- mining model
- export, 7-10
- import, 7-10
- mining model object, 6-4
- mining models
- export, 9-2
- import, 9-2
- moving, 9-2
- mining result object, 6-5
- missing values, 2-7
- handling, 2-7
- mixture model, 4-5
- model apply
- Java interface, 5-3
- Java interface (figure), 5-4
- model building
- Java interface, 5-2
- model building (figure), 5-3
- model export
- Java interface, 5-5
- native, 9-2
- PMML, 5-6, 9-2
- model import
- Java interface, 5-5
- PMML, 5-6, 9-2
- model seeker, 3-12
- model states, 3-8
- CompleteMultiFeature, 3-8
- CompleteSingleFeature, 3-8
- IncompleteSingleFfature, 3-8
- NaiveBayes, 3-8
- model testing
- java interface, 5-3
- lift, 5-3
- models
- apply, 3-2
- association, 4-7
- building, 3-1
- classification, 3-1
- clustering, 4-1
- DBMS_DATA_MINING, 7-2
- descriptive, 1-2, 4-1
- export, 7-10
- import, 7-10
- predictive, 1-2, 3-1
- training, 3-1
- most frequent items, 2-11
- multi-record case, 2-3
- multi-record case data table format, 2-2
- Java interface, 2-3
- views, 2-4
N
- Naive Bayes algorithm, 3-3
- NavieBayes
- ABN model state, 3-8
- NB, 3-3
- nested table format, 2-3
- NMF, 4-11
- non-negative matrix factorization, 4-11
- nontransactional, 6-1
- see single-record case, 2-2
- normalization, 2-12
- null values, 2-7
- numerical data type, 2-2, 4-2, 4-6
O
- O-cluster
- apply, 4-6
- attribute types, 4-6
- binning, 4-6
- compared with k-means, 4-7
- data preparation, 4-6
- scoring, 4-6
- O-Cluster algorithm, 4-5
- ODM, 1-1
- ODM features, A-2
- ODM programming environments, A-4
- Oracle Data Mining
- scoring engine, 9-1
- Oracle data mining, 1-1
- data, 2-1
- orthogonal partitioning clustering, 4-5
- outliers, 2-8
P
- PDS, 6-1
- physical data specification, 6-1
- PL/SQL interface
- algorithms, 7-3
- functions, 7-3
- PMML, 5-6
- export, 9-2
- import, 9-2
- Java interface, 5-6
- Predictive Model Markup Language, 5-6
- predictive models, 1-2, 3-1
- prepared data, 2-10
- preprocessing
- data, 4-1
- prior probabilities, 7-4
- priors, 3-3, 7-4
- programming environments, A-4
R
- rare events
- association models, 4-8
- receiver operating characteristics, 7-8
- figure, 7-9
- statistics, 7-10
- regression, 3-10
- algorithm, 3-10
- text mining, 8-4
- regression models
- test, 7-10
- ROC, 7-8
- rules
- Adaptive Bayes Network, 3-5
S
- scoring, 3-2, 4-5
- in applications, 9-1
- O-Cluster, 4-6
- scoring data, 3-2, 9-1
- scoring engine, 9-1
- application deployment, 9-3
- features, 9-1
- installation, 9-1
- use, 9-3
- sequence alignment, 10-1
- ODM capabilities, 10-2
- sequence search, 10-1
- ODM capabilities, 10-2
- setting tables, 7-3
- settings
- support vector machine, 3-9
- single-record case, 2-3
- single-record case format, 2-2
- sparse data, 2-8, 4-9
- summarization, 4-7
- in k-means, 4-5
- support
- of association rule, 4-8
- support vector machine, 3-9
- data preparation, 3-9
- regression, 3-10
- settings, 3-9
- SVM, 3-9
T
- targets, 2-7
- TBLASTN, 10-2
- TBLASTX, 10-2
- test result object, 6-5
- testing models, 3-2
- DBMS_DATA_MINING, 7-6
- text mining, 4-11, 8-1
- association, 8-4
- classification, 8-3
- clustering, 8-3
- feature extraction, 4-11, 8-4
- ODM support, 8-1
- regression, 8-4
- support (figure), 8-5
- transactional, 6-1
- see multi-record case, 2-3
U
- unbalanced approach
- k-means, 4-3
- unprepared data, 2-10
- unstructured attributes, 2-2
- unstructured data, 2-5
W
- wide data, 2-3
- fixed collection types, 2-4
- nested table format, 2-3
- winsorizing, 2-11
- figure, 2-12