AIM2: Improved implementation of AIM

We present AIM2-F, an improved implementation of AIM-F [4] algorithm for mining frequent itemsets. Past studies have proposed various algorithms and techniques for improving the efficiency of the mining task. We have presented AIM-F at FIMI'03, a combination of some 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 AIM2F outperforms existing algorithm implementations on various datasets.

• Type: Conference
• Year: 2004
• Where: FIMI
• Authors: Sagi Shporer
• Keywords: Algorithm, Algorithm Main Features, Data Mining, FIMI 2004, Mining Frequent Itemsets