Frequent pattern mining based on Imperative Tabularized Apriori Algorithm (ITAA)

摘要

The frequent pattern mining algorithms determine the frequent patterns from a transaction database. When the database is updated, the frequent patterns should be updated as well. However, running the frequent pattern mining algorithms with every update is not adequate. This is called the imperative update problem of frequent patterns and the solution is to formulate an algorithm that can with vitality mine the frequent patterns. In this study, an imperative frequent pattern mining algorithm, which is called Imperative Tabularized Apriori Algorithm (ITAA), is proposed and explained. Performance evaluation is given to prove the proposed work.