Data mining — Classification

With the Classification algorithms, you can create, validate, or test classification models. For example, you can analyze why a certain classification was made, or you can predict a classification for new data.

You can use the Intelligent Miner® Visualizer to view and analyze the classification models, or you can use the classification models to score new data records, that means, to predict class labels for these new data records.

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 'lapsed" or 'has not lapsed'. 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 attribute values 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 task flow looks like this:

The insurance company uses an Intelligent Miner classification training run to identify typical combinations of attribute values 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 useIntelligent Miner to apply the tested model to new data. Intelligent Miner predicts the customers who are likely to let their insurance lapse in the future.