An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 52

patterns. Its application was extended to also cover any other ''basket-like'' problem
from various other industries. For example:
• In banking, it can be used for finding common product combinations owned by
customers.
• In telecommunications, for revealing services that usually go together.
• In web analysis, for finding web pages accessed in single visits.
Association models are considered as unsupervised techniques since they do
not involve a single output field to be predicted. They analyze product affinity
tables: that is, multiple fields that denote product/service possession. These fields
are at the same time considered as inputs and outputs. Thus, all products are
predicted and act as predictors for the rest of the products.
According to the business scope and the selected level of analysis, association
models can be applied to:
• Transaction or order data - data summarizing purchases at a transaction level,
for instance what is bought in a single store visit.
• Aggregated information at a customer level - what is bought during a specific
time period by each customer or what is the current product mix of each (bank)
customer.
Product Groupings
In general, these techniques are rarely applied directly to product codes.
They are usually applied to product groups. A taxonomy level, also referred to
as a hierarchy or grouping level, is selected according to the defined business
objective and the data are grouped accordingly. The selected product group-
ing will also determine the type of generated rules and recommendations.
A typical modeling dataset for an association model has the tabular format
shown in Table 2.10. These tables, also known as basket or truth tables, contain
categorical, flag (binary) fields which denote the presence or absence of specific
items or events of interest, for instance purchased products. The fields denoting
product purchases, or in general event occurrences, are the content fields. The
analysis ID field, here the transaction ID, is used to define the unit or level
of the analysis. In other words, whether the revealed purchase patterns refer
to transactions or customers. In tabular data format, the dataset should contain
aggregated content/purchase information at the selected analysis level.