An Overview of Data Mining Techniques - Data Mining Techniques in CRM: Inside Customer Segmentation - page 53

Table 2.10 The modeling dataset for association modeling - a basket table.

Input-output fields

Analysis ID field Content fields

Transaction ID Product 1 Product 2 Product 3 Product 4

101 True False True False

102 True False False False

103 True False True True

104 True False False True

105 True False False True

106 False True True False

107 True False True True

108 False False True True

109 True False True True

In the above example, the goal is to analyze purchase transactions and identify
rules which describe the shopping cart patterns. We also assume that products are
grouped into four supercategories.

Analyzing Raw Transactional Data with Association Models

Besides basket tables, specific algorithms, like the a priori association model,
can also directly analyze transactional input data. This format requires the
presence of two fields: a content field denoting the associated items and an
analysis ID field that defines the level of analysis. Multiple records are linked
by having the same ID value. The transactional modeling dataset for the
simple example presented above is listed in Table 2.11.

Table 2.11 A transactional modeling
dataset for association modeling.

Input-output field

Analysis ID field Content field

Transaction ID Products

101 Product 1

101 Product 3