DARM: Decremental Association Rules Mining

Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding fre-quent item sets in very large transaction databases have been developed. Although many techniques were proposed for maintenance of the discovered rules when new transactions are added, little work is done for maintaining the discov-ered rules when some transactions are deleted from the database. Updates are fundamental aspect of data management. In this paper, a decremental association rules mining algorithm is present for updating the discovered association rules when some transactions are removed from the original data set. Extensive experiments were conducted to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm is efficient and outperforms other well-known algorithms