Association rule mining involves the identification of frequent itemsets and the generation of association rules. A frequent itemset is a set of items that appear together in a dataset with a frequency above a certain threshold. An association rule is a probabilistic statement about the occurrence of one item given the presence of another item.
Steps Involved The process typically involves the following steps:
Data Preprocessing: Clean and format the data for analysis. Frequent Itemset Generation: Identify sets of items that frequently occur together. Rule Generation: Generate rules that describe the relationships between the frequent itemsets. Evaluation: Assess the quality of the generated rules using metrics like support, confidence, and lift.
