Index
A B C D E F G J K L M N O P R S T U W
A
- Adaptive Bayes Network algorithm, 4-6, 4-23
- algorithm
- mining, 4-3
- algorithm settings
- Adaptive Bayes Network, 4-6
- k-means, 4-8
- Naive Bayes, 4-7
- Non-Negative Matrix Factorization (NMF), 4-8
- Support Vector Machines, 4-7
- apply
- input and output datasets, 3-10
- model, 2-6
- results, 4-13
- Attribute Importance
- using, 2-4
- attributes
- clustering models, B-1
- find, 2-4
- target, 4-17
- types, 4-17
- using, 2-4
- automated binning, 3-14
B
- binning, 2-4
- automated, 3-14
- embedded, 3-16
- external, 3-14
- for k-means, B-1
- BLAST
- NCBI, 6-1
- ODM, 6-2
- output, 6-6
- sample data, 6-8
- BLAST table functions
- summary of, 6-13
- BLASTN_ALIGN table function, 6-5, 6-23
- BLASTN_MATCH table function, 6-2, 6-14
- BLASTP_ALIGN table function, 6-27
- BLASTP_MATCH table function, 6-4, 6-17
- build results, 4-12
C
- CLASSPATH for ODM, 2-1
- clinical data table, 4-16
- clustering models
- attributes, B-1
- tips, B-1
- compute lift, 3-9
- constants summary
- algorithm settings, 4-22
- mining functions, 4-21
- cost matrix, 3-12, 4-11
D
- data
- mining, 4-14
- preparation, 2-2, 3-14
- data mining server (DMS), 3-1, 3-16
- data transformations, 4-17
- DBMS_DATA_MINING sample programs, 5-1
- DNA sequences, 6-2
E
- embedded binning, 3-16
- errors summary, 4-25
- export and import
- model, 4-27
- external binning, 3-14
F
- feature extraction, B-6
- function
- mining, 4-3
- function settings
- summary of, 4-5
- functions and algorithms
- summary of, 4-4
G
- gene expression data table, 4-16
- genetic codes, 6-8
J
- Java sample programs, 3-17
K
- k-Means algorithm, 4-8, 4-24
L
- lift computation, 3-9
- lift results, 3-10
- limitations
- Model Export and Import, 4-28
- limitations and rules
- DBMS_DATA_MINING, 4-18
LocationAccessData (LAD), 3-2
M
- matching
- sequences, 6-1
- matrix factorization, B-6
- mean absolute error, 4-13
- mining
- data, 4-14
- models, 4-3
- operations, 4-12
- results, 4-12
MiningFunctionSettings object, 3-3MiningModel object, 3-7MiningTask object, 3-5- model
- apply, 2-6, 3-1, 3-10, 3-11
- data format, 2-6
- building, 2-4, 3-1, 3-6
- mining, 4-3
- score, 3-1
- scoring, 3-10, 3-11
- testing, 3-7
- results, 3-8
- Model Export and Import, 4-27
- limitations and prerequisites, 4-28
N
- Naive Bayes algorithm, 4-7, 4-23
- sample programs, 3-1
- NCBI, 6-1
- NMF models
- tips, B-6
- Non-Negative Matrix Factorization (NMF) algorithm, 3-16, 4-8, 4-24
- normalization, 2-3, 3-14
- NMF, 2-3, B-7
- Support Vector Machines, 2-3
O
- ODM BLAST, 6-2
- ODM PL/SQL sample programs, 5-1, 5-3
- ODM programming
- basic usage, 3-1
- common tasks, 2-1
- Java interface, 2-1
- PLSQL interface, 4-1
- ODM programs
- compiling, 2-1
- executing, 2-1
- operations
- mining, 4-12
- output of BLAST query, 6-6
P
- performance, 4-18
- PLSQL interface, 4-1
- preparation
- of data, 3-14
- prerequisites
- Model Export and Model Import, 4-28
- prior probabilities, 3-13, 4-10
- protein sequences, 6-4
R
- real-time scoring, 3-12
- results
- apply, 4-13
- build, 4-12
- lift, 3-10
- mining, 4-12
- test, 4-13
- root mean square error, 4-13
- rules and limitations
- DBMS_DATA_MINING, 4-18
S
- sample programs
- DBMS_DATA_MINING, 5-1
- Java, 3-17
- ODM PL/SQL, 5-1
- scoring
- data, 2-6, 3-10
- real-time, 3-12
- sequence matching, 6-1
- sequences
- DNA, 6-2
- protein, 6-4
- settings table, 4-4
- SH schema, 5-3
- subprograms
- DBMS_DATA_MINING, 4-26
- Support Vector Machines algorithm, 3-16, 4-7, 4-23
- normalizatioin, 2-3
- SVM models
- tips, B-2
T
- target attributes, 4-17
- target value, 3-9
- TBLAST_ALIGN table function, 6-30
- TBLAST_MATCH table function, 6-17, 6-20
- test results, 4-13
- text mining, 3-16, 7-1
- Top-N Frequency, 2-3
- transformations, 4-17
U
- user views summary, 4-26
W
- wide data, 4-14
