An Algorithm of Top-k High Utility Itemsets Mining over Data Stream

摘要

Existing top-k high utility itemset (HUI) mining algorithms generate candidate itemsets in the mining process; their timeandspace performance might be severely affected when the dataset is large or contains many long transactions; and when applied to data streams, the performance of corresponding mining algorithm is especially crucial. To address this issue, propose a sliding window based top-k HUIs mining algorithm TOPK-SW; it first stores each batch data of current window as well as the items’ utility information to a tree called HUI-Tree, which ensures effective retrieval of utility values without re-scan the dataset, so as to efficiently improve the mining performance. TOPK-SW was tested on 4 classical datasets; results show that TOPK-SW outperforms existing algorithms significantly in both time and space efficiency, especially the time performance improves over 1 order of magnitude.