Examples of prediction tasks addressed by the data mining techniques dis cussed in this book include: Predicting the size of the balance that will be transferred if a credit card prospect accepts a balance transfer offer Predicting which customers will leave within the next 6 months Predicting which telephone subscribers will order a value-added ser vice such as three-way calling or voice mail Most of the data mining techniques discussed in this book are suitable for use in prediction so long as training data is available in the proper form.
The Why and What Is Data Mining? choice of technique depends on the nature of the input data, the type of value to be predicted, and the importance attached to explicability of the prediction. Affinity Grouping or Association Rules The task of affinity grouping is to determine which things go together. The prototypical example is determining what things go together in a shopping cart at the supermarket, the task at the heart of market basket analysis. Retail chains can use affinity grouping to plan the arrangement of items on store shelves or in a catalog so that items often purchased together will be seen together. Affinity grouping can also be used to identify cross-selling opportunities and to design attractive packages or groupings of product and services. Affinity grouping is one simple approach to generating rules from data. If two items, say cat food and kitty litter, occur together frequently enough, we can generate two association rules: