A Bidirectional Process Algorithm for Mining Probabilistic Frequent Itemsets

Nowadays, frequent item set mining is the major task in association rule mining. With the observation that the support plays an important role in mining frequent item sets, in this paper, we review the previous efficient algorithms and study the effect of different order of support in the performance of frequent item sets mining algorithms and propose our improved schedule. In our new algorithm, items are sorted in descending order according to the frequencies in transaction cache while item sets use ascending order of support during support count. Compared with other algorithms, the results of experiments show that the new algorithm gains better performance on on well-known benchmark data sets.