An Algorithm for Updating Discovered Association Rules in Data Mining

데이타 마이닝에서 기존의 연관 규칙을 갱신하는 앨고리듬 개발

There have been many studies on efficient discovery of association rules in large databases. However, it is nontrivial to maintain such discovered rules in large databases because a database may allow frequent or occasional updates and such updates may not only invalidate some existing strong association rules but also turn some weak rules into strong ones. The major idea of updating algorithm is to resuse the information of the old large itemsets and to integrate the support information of the new large itemsets in order to substantially reduce the pool of candidate sets to be re-exmained. In this paper, an updating algorithm is proposed for efficient maintenance of discovered assocation rules when new transaction data are added to a transaction database. And superiority of the proposed updating algorithm will be shown by comparing with FUP algorithm that was already proposed.