2.2 Data Mining Data Dictionary Views
Lists Oracle Data Mining data dictionary views.
The data dictionary views for Oracle Data Mining are listed in the following table. A database administrator (DBA) and USER versions of the views are also available.
Table 2-1 Data Dictionary Views for Oracle Data Mining
2.2.1 ALL_MINING_MODELS
Describes an example of ALL_MINING_MODELS and shows a sample query.
The following example describes ALL_MINING_MODELS and shows a sample query.
Example 2-1 ALL_MINING_MODELS
describe ALL_MINING_MODELS
Name Null? Type
----------------------------------------- -------- ----------------------------
OWNER NOT NULL VARCHAR2(128)
MODEL_NAME NOT NULL VARCHAR2(128)
MINING_FUNCTION VARCHAR2(30)
ALGORITHM VARCHAR2(30)
CREATION_DATE NOT NULL DATE
BUILD_DURATION NUMBER
MODEL_SIZE NUMBER
PARTITIONED VARCHAR2(3)
COMMENTS VARCHAR2(4000)The following query returns the models accessible to you that use the Support Vector Machine algorithm.
SELECT mining_function, model_name
FROM all_mining_models
WHERE algorithm = 'SUPPORT_VECTOR_MACHINES'
ORDER BY mining_function, model_name;
MINING_FUNCTION MODEL_NAME
------------------------- --------------------
CLASSIFICATION PART2_CLAS_SAMPLE
CLASSIFICATION PART_CLAS_SAMPLE
CLASSIFICATION SVMC_SH_CLAS_SAMPLE
CLASSIFICATION SVMO_SH_CLAS_SAMPLE
CLASSIFICATION T_SVM_CLAS_SAMPLE
REGRESSION SVMR_SH_REGR_SAMPLE Related Topics
2.2.2 ALL_MINING_MODEL_ATTRIBUTES
Describes an example of ALL_MINING_MODEL_ATTRIBUTES and shows a sample query.
The following example describes ALL_MINING_MODEL_ATTRIBUTES and shows a sample query. Attributes are the predictors or conditions that are used to create models and score data.
Example 2-2 ALL_MINING_MODEL_ATTRIBUTES
describe ALL_MINING_MODEL_ATTRIBUTES Name Null? Type ----------------------------------------- -------- ---------------------------- OWNER NOT NULL VARCHAR2(128) MODEL_NAME NOT NULL VARCHAR2(128) ATTRIBUTE_NAME NOT NULL VARCHAR2(128) ATTRIBUTE_TYPE VARCHAR2(11) DATA_TYPE VARCHAR2(106) DATA_LENGTH NUMBER DATA_PRECISION NUMBER DATA_SCALE NUMBER USAGE_TYPE VARCHAR2(8) TARGET VARCHAR2(3) ATTRIBUTE_SPEC VARCHAR2(4000)
The following query returns the attributes of an SVM classification model named T_SVM_CLAS_SAMPLE. The model has both categorical and numerical attributes and includes one attribute that is unstructured text.
SELECT attribute_name, attribute_type, target
FROM all_mining_model_attributes
WHERE model_name = 'T_SVM_CLAS_SAMPLE'
ORDER BY attribute_name;
ATTRIBUTE_NAME ATTRIBUTE_TYPE TAR
------------------------- -------------------- ---
AFFINITY_CARD CATEGORICAL YES
AGE NUMERICAL NO
BOOKKEEPING_APPLICATION NUMERICAL NO
BULK_PACK_DISKETTES NUMERICAL NO
COMMENTS TEXT NO
COUNTRY_NAME CATEGORICAL NO
CUST_GENDER CATEGORICAL NO
CUST_INCOME_LEVEL CATEGORICAL NO
CUST_MARITAL_STATUS CATEGORICAL NO
EDUCATION CATEGORICAL NO
FLAT_PANEL_MONITOR NUMERICAL NO
HOME_THEATER_PACKAGE NUMERICAL NO
HOUSEHOLD_SIZE CATEGORICAL NO
OCCUPATION CATEGORICAL NO
OS_DOC_SET_KANJI NUMERICAL NO
PRINTER_SUPPLIES NUMERICAL NO
YRS_RESIDENCE NUMERICAL NO
Y_BOX_GAMES NUMERICAL NO Related Topics
2.2.3 ALL_MINING_MODEL_PARTITIONS
Describes an example of ALL_MINING_MODEL_PARTITIONS and shows a sample query.
ALL_MINING_MODEL_PARTITIONS and shows a sample query.
Example 2-3 ALL_MINING_MODEL_PARTITIONS
describe ALL_MINING_MODEL_PARTITIONS Name Null? Type ----------------------------------------- -------- ---------------------------- OWNER NOT NULL VARCHAR2(128) MODEL_NAME NOT NULL VARCHAR2(128) PARTITION_NAME VARCHAR2(128) POSITION NUMBER COLUMN_NAME NOT NULL VARCHAR2(128) COLUMN_VALUE VARCHAR2(4000)
The following query returns the partition names and partition key values for two partitioned models. Model PART2_CLAS_SAMPLE has a two column partition key with system-generated partition names.
SELECT model_name, partition_name, position, column_name, column_value
FROM all_mining_model_partitions
ORDER BY model_name, partition_name, position;
MODEL_NAME PARTITION_ POSITION COLUMN_NAME COLUMN_VALUE
-------------------- ---------- -------- -------------------- ---------------
PART2_CLAS_SAMPLE DM$$_P0 1 CUST_GENDER F
PART2_CLAS_SAMPLE DM$$_P0 2 CUST_INCOME_LEVEL HIGH
PART2_CLAS_SAMPLE DM$$_P1 1 CUST_GENDER F
PART2_CLAS_SAMPLE DM$$_P1 2 CUST_INCOME_LEVEL LOW
PART2_CLAS_SAMPLE DM$$_P2 1 CUST_GENDER F
PART2_CLAS_SAMPLE DM$$_P2 2 CUST_INCOME_LEVEL MEDIUM
PART2_CLAS_SAMPLE DM$$_P3 1 CUST_GENDER M
PART2_CLAS_SAMPLE DM$$_P3 2 CUST_INCOME_LEVEL HIGH
PART2_CLAS_SAMPLE DM$$_P4 1 CUST_GENDER M
PART2_CLAS_SAMPLE DM$$_P4 2 CUST_INCOME_LEVEL LOW
PART2_CLAS_SAMPLE DM$$_P5 1 CUST_GENDER M
PART2_CLAS_SAMPLE DM$$_P5 2 CUST_INCOME_LEVEL MEDIUM
PART_CLAS_SAMPLE F 1 CUST_GENDER F
PART_CLAS_SAMPLE M 1 CUST_GENDER M
PART_CLAS_SAMPLE U 1 CUST_GENDER U Related Topics
2.2.4 ALL_MINING_MODEL_SETTINGS
Describes an example of ALL_MINING_MODEL_SETTINGS and shows a sample query.
The following example describes ALL_MINING_MODEL_SETTINGS and shows a sample query. Settings influence model behavior. Settings may be specific to an algorithm or to a mining function, or they may be general.
Example 2-4 ALL_MINING_MODEL_SETTINGS
describe ALL_MINING_MODEL_SETTINGS Name Null? Type ----------------------------------------- -------- ---------------------------- OWNER NOT NULL VARCHAR2(128) MODEL_NAME NOT NULL VARCHAR2(128) SETTING_NAME NOT NULL VARCHAR2(30) SETTING_VALUE VARCHAR2(4000) SETTING_TYPE VARCHAR2(7)
The following query returns the settings for a model named SVD_SH_SAMPLE. The model uses the Singular Value Decomposition algorithm for feature extraction.
SELECT setting_name, setting_value, setting_type
FROM all_mining_model_settings
WHERE model_name = 'SVD_SH_SAMPLE'
ORDER BY setting_name;
SETTING_NAME SETTING_VALUE SETTING
------------------------------ ------------------------------ -------
ALGO_NAME ALGO_SINGULAR_VALUE_DECOMP INPUT
ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO DEFAULT
ODMS_SAMPLING ODMS_SAMPLING_DISABLE DEFAULT
PREP_AUTO OFF INPUT
SVDS_SCORING_MODE SVDS_SCORING_SVD DEFAULT
SVDS_U_MATRIX_OUTPUT SVDS_U_MATRIX_ENABLE INPUT Related Topics
2.2.5 ALL_MINING_MODEL_VIEWS
Describes an example of ALL_MINING_MODEL_VIEWS and shows a sample query.
ALL_MINING_MODEL_VIEWS and shows a sample query. Model views provide details on the models.
Example 2-5 ALL_MINING_MODEL_VIEWS
describe ALL_MINING_MODEL_VIEWS
Name Null? Type
----------------------------------------- -------- ----------------------------
OWNER NOT NULL VARCHAR2(128)
MODEL_NAME NOT NULL VARCHAR2(128)
VIEW_NAME NOT NULL VARCHAR2(128)
VIEW_TYPE VARCHAR2(128)
The following query returns the model views for a model SVD_SH_SAMPLE. The model uses the Singular Value Decomposition algorithm for feature extraction.
SELECT view_name, view_type
FROM all_mining_model_views
WHERE model_name = 'SVD_SH_SAMPLE'
ORDER BY view_name;
VIEW_NAME VIEW_TYPE
------------------------- --------------------------------------------------
DM$VESVD_SH_SAMPLE Singular Value Decomposition S Matrix
DM$VGSVD_SH_SAMPLE Global Name-Value Pairs
DM$VNSVD_SH_SAMPLE Normalization and Missing Value Handling
DM$VSSVD_SH_SAMPLE Computed Settings
DM$VUSVD_SH_SAMPLE Singular Value Decomposition U Matrix
DM$VVSVD_SH_SAMPLE Singular Value Decomposition V Matrix
DM$VWSVD_SH_SAMPLE Model Build Alerts Related Topics
2.2.6 ALL_MINING_MODEL_XFORMS
Describes an example of ALL_MINING_MODEL_XFORMS and provides a sample query.
ALL_MINING_MODEL_XFORMS and provides a sample query.
Example 2-6 ALL_MINING_MODEL_XFORMS
describe ALL_MINING_MODEL_XFORMS Name Null? Type ----------------------------------------- -------- ---------------------------- OWNER NOT NULL VARCHAR2(128) MODEL_NAME NOT NULL VARCHAR2(128) ATTRIBUTE_NAME VARCHAR2(128) ATTRIBUTE_SUBNAME VARCHAR2(4000) ATTRIBUTE_SPEC VARCHAR2(4000) EXPRESSION CLOB REVERSE VARCHAR2(3)
The following query returns the embedded transformations for a model PART2_CLAS_SAMPLE.
SELECT attribute_name, expression
FROM all_mining_model_xforms
WHERE model_name = 'PART2_CLAS_SAMPLE'
ORDER BY attribute_name;
ATTRIBUTE_NAME
-------------------------
EXPRESSION
--------------------------------------------------------------------------------
CUST_INCOME_LEVEL
CASE CUST_INCOME_LEVEL WHEN 'A: Below 30,000' THEN 'LOW'
WHEN 'L: 300,000 and above' THEN 'HIGH'
ELSE 'MEDIUM' END Related Topics