AIM: Another Itemset Miner

We present a new algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the efficiency 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, diffset, parent equivalence pruning, dynamic reordering and projection. Experimental testing suggests that our algorithm and implementation significantly outperform existing algorithms/implementations.

• Type: Conference
• Year: 2003
• Where: FIMI
• Authors: Amos Fiat, Sagi Shporer
• Keywords: Algorithm, Algorithm Main Features, Data Mining, FIMI 2003, Mining Frequent Itemsets