UDS-FIM: An Efficient Algorithm of Frequent Itemsets Mining over Uncertain Transaction Data Streams

Abstract

In this paper, we study the problem of finding frequent itemsets from uncertain data streams. To the best of our knowledge, the existing algorithms cannot compress transaction itemsets to a tree as compact as the classical FP-Tree, thus they need much time and memory space to process the tree. To address this issue, we propose an algorithm UDS-FIM and a tree structure UDS-Tree. Firstly, UDS-FIM maintains probability values of each transactions to an array; secondly, compresses each transaction to a UDS-Tree in the same manner as an FP-Tree (so it is as compact as an FP-Tree) and maintains index of probability values of each transaction in the array to the corresponding tail-nodes; lastly, it mines frequent itemsets from the UDS-Tree without additional scan of transactions. The experimental results show that UDS-FIM has achieved a good performance under different experimental conditions in terms of runtime and memory consumption.