And because the technique derived from the analysis supermarket data

Predictive data mining is applied to a range of techniques that find rela-tionships between a specific variable (called the target variable) and the other variables in your data. The following are examples of predictive min-ing techniques:

Classification. This term describes the assignment of data records into predefined categories—for example, assigning customers to pre- defined market segments, risk classes, or product usage classes. In this case, the target variable is the category and the techniques dis- cover the relationship between the other variables and the target cat- egory. When a new record is to be classified, the technique
determines the category and the probability that the record belongs to the category. Classification techniques include decision trees and neural and radial basis function (RBF) classifiers.

Step 6: Interpret the Results

The results from performing any type of data mining can provide a wealth of information that can be difficult to interpret. Therefore, this step often requires assistance from a business expert who can translate the mining results back into the business context. Because we do not expect the busi-ness analyst to necessarily be a mining expert, it is important that the results are presented in such a way that they are relatively easy to interpret. To assist in this process, you have at your disposal a range of tools that enable you to visualize the results and to provide the necessary statistical information necessary for facilitating the interpretation. Figure 14.4 illus-trates one of the visualization techniques used by Intelligent Miner.