An Improved Vertical Algorithm for Frequent Itemset Mining from Uncertain Database
Junrui Yang, Yingjie Zhang, Yanjun Wei · 2017
For the reason that the algorithm PFIM needs to scan database repeatedly, to produce a great deal of redundant candidate itemset, and to compute more time-complexity of frequent probability, an improved algorithm UPro-Eclat which is based on PFIM and Eclat is proposed. It uses a vertical mining method which is extension-based, adds probabilistic information in Tid, builds recursively the subset of search tree, and mines probabilistic frequent pattern by depth-first traversa. The algorithm UPro-Eclat can swiftly find probabilistic frequent itemset rather than compute their probability in each possible world.