Modeling Techniques

Modeling techniques are based around the use of algorithms - sequences of instructions for solving specific problems. You use a particular algorithm to create that type of model. There are three main classes of modeling technique, and IBM® SPSS® Modeler provides several examples of each:

• Supervised
• Association
• Segmentation (sometimes known as “clustering”)

Supervised models use the values of one or more input fields to predict the value of one or more output, or target, fields. Some examples of these techniques are: decision trees (C&R Tree, QUEST, CHAID and C5.0 algorithms), regression (linear, logistic, generalized linear, and Cox regression algorithms), neural networks, support vector machines, and Bayesian networks.

Association models find patterns in your data where one or more entities (such as events, purchases, or attributes) are associated with one or more other entities. The models construct rule sets that define these relationships. Here the fields within the data can act as both inputs and targets. You could find these associations manually, but association rule algorithms do so much more quickly, and can explore more complex patterns. Apriori and Carma models are examples of the use of such algorithms. One other type of association model is a sequence detection model, which finds sequential patterns in time-structured data.

Segmentation models divide the data into segments, or clusters, of records that have similar patterns of input fields. As they are only interested in the input fields, segmentation models have no concept of output or target fields. Examples of segmentation models are Kohonen networks, K-Means clustering, two-step clustering and anomaly detection.