Frequent item sets mining plays an important role in association rules mining. A variety of algorithms for finding frequent
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 discovered
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.