Frequent Pattern Mining Algorithm based on Multi Minimum Support

Abstract

An improved multi minimum support frequent pattern mining algorithm IMISFP-growth is proposed. Firstly, preprocessing the items in the transaction database before constructing the tree, deleting those items whose support is less than the minimum item support, and constructing multiple support trees using the remaining frequent items. Then a new method of constructing multiple item tree based on intersection rules is proposed. This method no longer uses a specific standard arrangement item to generate tree, but constructs a tree by the principle of intersection every time a new transaction item set is input. Finally, the IMISFP-growth algorithm is compared with the CFP-growth++ algorithm on five different databases. The experimental results show that the improved algorithm is superior to the CFP-growth++ algorithm in terms of running time, memory consumption and scalability.