A data mining analyst wants to obtain the rules contained
in historic data. A specific mining algorithm is selected, configured,
and applied to a specified set of input data. The execution of the
mining algorithm is called training phase. The result of the training
phase is a data mining model.
You can use the model for the following tasks:
- Computing scores for new records (applying the model)
- Graphically exploring the model with the Intelligent Miner® Visualizer)
- Extracting the rules as database rows by using Intelligent
Miner table
functions
In Figure 1 the Modeling buildModel function
receives a BldTask object that describes the physical
(MiningData) and logical (LogicalDataSpec)
schema of the input table as well as the settings that configure the
mining algorithm. It produces a mining model.
Figure 1. SQL API in Intelligent
Miner
The training phase consists of the following steps:
- Specifying mining data
Consists of source table name, column
names, and types, optional alias names
- Defining logical data specifications
Holds for each field
in the source table the mining field name and mining field type. It
also optionally holds name mappings, taxonomies, or numerical field
properties. A mining field name matches an alias name in mining data
to map the fields during the training run.
- Defining mining settings
Consists of the logical data specification
and additional mining function/algorithm, specific settings, such
as:
- Transaction/item field and confidence and support for Association
- Target field for Classification or Regression
- Number of clusters for Clustering
- Defining mining tasks
Combines mining settings with mining
data. Additionally some control parameters can be set such as the
location for error or progress information. For example:
INSERT INTO IDMMX.ClasTasks (ID,TASK)
WITH MyData (MyDataCol) AS (VALUES(
IDMMX.DM_MiningData()..DM_defMiningData('HEART')))
SELECT 'HeartTask', IDMMX.DM_ClasBldTask()..
DM_defClasBldTask(MyDataCol,cast(NULL as IDMMX.DM_MiningData),
IDMMX.DM_ClasSettings()..DM_useClasDataSpec(MyDataCol..DM_genDataSpec())..
DM_setClasTarget('DISEASED')) FROM MyData;
- Building and storing mining models
Executing a predefined mining
task. The mining model is written into a table during execution. For
example:
CALL IDMMX.DM_buildClasModelCmd( 'IDMMX.CLASTASKS', 'TASK','ID','HeartTask',
'IDMMX.CLASSIFMODELS','MODEL','MODELNAME','CustModel');