Association rule mining is used to analyze relationships between data and uncover patterns. Sequential methods form the foundation for algorithms that generate frequent patterns in databases. The Apriori algorithm is a two-step process that generates candidate item sets and then scans the database to determine frequent item sets. Variations include hash-based Apriori, which uses hashing to reduce candidate sets, and partition-based Apriori, which divides the database into partitions to reduce scans. Parallel methods allow algorithms to run across distributed memory systems.