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Association rules: Introduction, Large item sets, Basic algorithms, Apriori Analysis

Association rule mining is a data mining technique used to discover interesting relationships between variables in large datasets. It is used to identify patterns or relationships between variables that occur together more frequently than would be expected by chance.

The process of association rule mining involves finding frequent item sets, which are sets of items that frequently appear together in the dataset. These frequent item sets are then used to generate association rules, which describe the relationships between the items. Association rules consist of two parts: an antecedent (the items that are present) and a consequent (the items that are predicted to occur based on the presence of the antecedent).

There are several algorithms used for association rule mining, including the Apriori algorithm, the FP-Growth algorithm, and the Eclat algorithm.

The Apriori algorithm is a basic algorithm used for association rule mining. It involves generating candidate item sets and pruning those item sets that do not meet the minimum support threshold. The support threshold is a user-defined parameter that determines the minimum frequency with which an item set must occur in the dataset to be considered frequent. The Apriori algorithm iteratively generates larger and larger frequent item sets until no more frequent item sets can be found.

Apriori analysis is used to analyze the results of association rule mining. It involves examining the generated association rules to determine their significance and usefulness. Some common measures used in Apriori analysis include support (the frequency of occurrence of the item set in the dataset), confidence (the conditional probability of the consequent given the antecedent), and lift (the ratio of the observed support to the expected support if the antecedent and consequent were independent).

Overall, association rule mining is a powerful technique in data mining that can be used to identify patterns and relationships in large datasets. The Apriori algorithm and Apriori analysis are important components of association rule mining that help in generating and interpreting the results.