Table of Contents
The process presents, in the case of Extended Bakery dataset,
how can association rules be obtained from a transaction dataset. In transaction datasets,
items from the possible ones are emphasized that are part of the transaction, rather than those
that are lacking. In the Enterprise Miner™, such dataset
must be defined as transaction dataset and it must include a
ID variable and
Target target variables that must be nominal. The separate market baskets can
be formed by
ID variable. The association rule mining (also called market basket analysis)
can be carried out by the
Market Basket operator. First, the frequent itemsets are
extracted, then, the significant association rules are discovered based on these itemsets.
Extended Bakery [Extended Bakery]
Market Basket operator on the dataset of
20000 records the
following results are given.
The significant association rules can be discovered on the basis of the frequent itemsets. We can select which criteria we would like to consider in order to find the appropriate rules. The defaults is the confidence level of the rules, but other criteria can also be applied to filter the discovered association rules. Based on the established rules deeper conclusions can be drawn from the relationships between the data. To support this inference several tools can be applied, e.g. the table of the association rules where we may filter the relevant rules by choosing different evaluation metric like support, confidence or lift value.