Classification is the process of automatically creating
a model of classes from a set of records that contain class labels.
The classification technique analyzes records that are already known
to belong to a certain class, and creates a profile for a member of
that class from the common characteristics of the records.
Intelligent Miner® enables
you to create classification models and to validate or test these
models, such as:
- To analyze why a certain classification was made
- To predict a classification for new data
You can use the Classification mining function to do the following,
for example:
- To approve or deny insurance claims
- To detect credit-card fraud
- To identify defects in images of manufactured parts
- To diagnose error conditions
Other suitable applications are target marketing, medical diagnosis,
medical treatment effectiveness, inventory replenishment, and store
location planning.
Use the Intelligent
Miner Visualizer
to view and analyze the classification models, or use Intelligent
Miner to
score new data records against a model.
For example, an insurance company has data about customers who
allowed their insurance to lapse and those who did not. How can the
company best use this information to identify such customers in the
future?
The insurance customers already belong to a certain class: they
are 'classified' as having allowed their insurance to lapse. The company
can use the Classification mining function to create a risk group
profile in the form of a data mining model. This profile, or model,
contains the common attributes of the lapsed customers, compared to
the other customers. The insurance company can then apply this profile
to new customers (as yet 'unclassified') to ascertain if they belong
to the risk group. The procedure is as follows:
- The insurance company uses an Intelligent
Miner classification
training run to identify the attributes of each defined customer risk
class, and to create a model.
- The insurer can use Intelligent
Miner to
test the accuracy of this model by applying the model to test data
with known customer risk classes.
- The insurer can use Intelligent
Miner to
apply the tested model to new data. This will predict which customers
are likely to let their insurance lapse in the future.