Evolution and Maintenance of Frequent Pattern Space When Transactions Are Removed

This paper addresses the maintenance of discovered frequent patterns when a batch of transactions are removed from the original dataset. We conduct an in-depth investigation on how the frequent pattern space evolves under transaction removal updates using the concept of equivalence classes. Inspired by the evolution analysis, an effective and exact algorithm TRUM is proposed to maintain frequent patterns. TRUM maintains frequent patterns efficiently by updating only the affected equivalence classes. Experimental results demonstrate that our algorithm outperforms representative state-of-the-art algorithms.