Generation of Association Rules Using Frequent Item Sets |
The generation of Association rule mining is todiscover the association rules. The frequent itemsets found in the previousstep are used to generate association rules. All the permutations andcombinations of the items present in the frequent itemsets are considered ascandidates for strong rules. A lot of rules will be generated in this way. Astrong rule is one that has minimum confidence which is computed by the FormulaThe main difference between Apriori and IAR algorithm is that IAR algorithmtakes user’s attribute preference for the resulting rules. Thereafter, the IARsearches for rules that contain the user specified attributes on the L.H.S. andderive other attributes in the database. If such a rule possesses highconfidence level then it could be valuable in the marketing context for theorganisation. In this way a lot of time can be saved and the user trusts morein the discovered rules.