Association Rule Mining and Apriori

Association rules are commonly written X → Y, with disjoint itemsets X and Y. The arrow describes a pattern in observed transactions, not a causal effect or a time order. In a basket dataset, support is the fraction of all baskets containing both X and Y. Confidence is the fraction of baskets containing X that also contain Y. Lift compares that confidence with how common Y is overall. A lift above one means Y appears more often with X than the simple overall Y rate would suggest in that dataset; it does not establish a mechanism. Use a constructed example with ten baskets. Bread appears in five, milk in four, and both in three. Support for bread → milk is 3/10 = 0.30. Confidence is 3/5 = 0.60. Milk's base rate is 4/10 = 0.40, so lift is 0.60/0.40 = 1.5. Reversing the rule changes confidence: milk → bread has 3/4 = 0.75, while support remains 0.30. Apriori, introduced by Agrawal and Srikant, searches itemsets by size. Its pruning principle is that if a set is frequent, all its subsets must also be frequent; therefore an infrequent subset rules out every larger candidate containing it. The algorithm repeatedly counts candidate itemsets against a minimum support threshold. This saves work but can still produce many candidates or scans when transactions contain many correlated items. FP-growth is another frequent-pattern method that compresses transactions in a tree and mines patterns without the same explicit candidate-generation process. A high-confidence rule can be uninteresting if Y is already common. A rare rule can be unstable even if its lift is large. Set support thresholds with the task and sample size in mind, and test patterns on later data. Data from loyalty accounts or online behavior can be incomplete and privacy-sensitive. Explain the population, time window and handling of missing transactions, and avoid turning a basket association into an unsupported claim that buying X causes Y.