Question

 

Explain the working of association rule mining using an illustrative transaction dataset. Describe the Apriori algorithm and interpret support, confidence and lift for generated rules. 

30 Jul 2026
Answer :
Word Count : 694
Association rule mining is a data mining technique used to discover interesting relationships, patterns, and associations among items in large transactional databases. It is widely applied in market basket analysis, recommendation systems, inventory management, healthcare, and web usage mining. The objective is to identify itemsets that frequently occur together and generate rules that help predict the occurrence of one item based on the presence of another. Consider the following transaction dataset: | Transaction ID | Items Purchased | | -------------- | ------------------------- | | T1 | Bread, Milk | | T2 | Bread, Diaper, Beer, Eggs | | T3 | Milk, Diaper, Beer, Cola | | T4 | Bread, Milk, Diaper, Beer | | T5 | Bread, Milk, Diaper, Cola | The first step in association rule mining is to identify frequent itemsets. A user-defined minimum support threshold determines whether an itemset is frequent. The algorithm scans the transaction database to count the
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