Abstract: The Apriori algorithm contains weaknesses such as often requiring a large number of repeated passes over the database to generate the frequent item sets and does not support the incremental updating. To solve these problems, a novel algorithm was proposed in this paper which is based on rough sets, single transaction combination itemsets and set operations for mining. It firstly uses the rough sets to reduce attributes, and then combines data item to each itemset from new decision table and marks it's tags. Finally, it calculates the support and confidence using set operations. This novel algorithm just needs to scanning the decision table only once, while effectively supporting the update of association rules mining. The results of application and experiments show that this novel algorithm is better than Apriori algorithm, it is an effective and fast algorithm for mining association rules.

Key words: Rough sets,Single transaction itemsets combination,Set operations,Updated mining