AIM: Another Itemset Miner.
Amos Fiat, Sagi Shporer · 2003
We present a new algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the e#ciency of the mining task. We integrate a combination of these techniques into an algorithm which utilize those techniques dynamically according to the input dataset. The algorithm main features include depth first search with vertical compressed database, di#set, parent equivalence pruning, dynamic reordering and projection. Experimental testing suggests that our algorithm and implementation significantly outperform existing algorithms /implementations.