Efficient Incremental High Utility Itemset Mining
Philippe Fournier‐Viger, Jerry Chun‐Wei Lin, Ted Gueniche, Prashant V.Barhate · 2015
High-utility itemset mining (HUIM) in transaction databases is an important data mining task with wide applications. However, most HUIM algorithms assume the unrealistic assumption that databases are static. To address this issue, algorithms have been designed to maintain high-utility itemsets in dynamic databases. However, these incremental algorithms still remain very costly in terms of execution time. In this paper, we address this problem by proposing an algorithm named EIHI (Efficient Incremental High-utility Itemset miner), which introduces several ideas to more efficiently maintain high-utility itemsets in dynamic databases. An experimental study on four datasets shows that EIHI is up to two orders of magnitude faster than the state-of-the-art HUI-LIST-INS algorithm.