Mining Frequent Patterns Based on Inverted List

In this paper, an Apriori algorithm is presented for mining frequent patterns based on inverted list. Compared with traditional Apriori algorithm and FP-growth algorithm, this algorithm has better efficiency and wider application range. Aimed at reducing the defect of traditional Apriori algorithm, this algorithm avoids lots of redundant operations with inverted list. This algorithm only needs scan data set twice and don´t need joining and pruning operations. Frequent item set is saved in each transaction frequent set TF, and insert next frequent single item one by one, then generate new possible frequent item set. In this way, lots of redundant operations can be reduced. The performance study shows that it is more efficient in both dense datasets and sparse datasets