Building mining models comprises various tasks. These tasks
are represented in the steps of the data mining process.
Mining data specification
You must select and specify the
data that you want to use for building
or testing mining models. The specification of the input data includes
the
information that is required for the training run, for example, the
name of
the source table and information about the columns in this
table.
Logical data specifications
You must define logical data specifications for
the data that you want to use for training runs. For each field in
the physical data, a logical definition is required that enables the
field to be used in the training run. This logical definition is contained
in the logical data specification.
Filtering rules
You can filter association
rules or sequence rules to display only
the data that you are interested in.
Rule filter constraints
You can limit the rules to be
included in a model by applying rule
filters in an Associations training run or in a Sequence Rules training
run.
Defining mining settings
You must define mining settings for your training mining
run. The mining settings contain the specific instructions for the
mining function that you want to use. The definitions and roles of
the fields also belong to the settings specification. This means that
the logical data specification is part of the mining settings.
Defining mining settings
You must define mining settings for your training mining
run. The mining settings contain the specific instructions for the
mining function that you want to use. The logical data specification
is part of the mining settings.
Defining mining tasks
A mining task brings together the information that is required
to start a training run and to compute the mining model. This information
consists of the mining settings and the input data definition.
Building and storing mining models Intelligent Miner® provides
predefined stored procedures to build mining models and to store them
in database tables.
Testing a model and analyzing its quality
You can test a model (classification or regression models)
by running a test task. You can also analyze the quality of a model
by extracting quality information from the test result or the model.
Working with mining models and test results
You can explore mining models and apply them to new data.
You can also deploy mining models or test results by extracting information.