Authors: Huowang Chen, Yuejin Yan, Zhoujun Li
Tags: 2004, conceptual modeling
Maximal frequent itemsets mining is a fundamental and important problem in many data mining applications. Since the MaxMiner algorithm introduced the enumeration trees for MFI mining in 1998, there have been several methods proposed to use depth-first search to improve performance. This paper presents FIMfi, a new depth-first algorithm based on FP-tree and MFI-tree for mining MFI. FIMfi adopts a novel item ordering policy for efficient lookaheads pruning, and a simple method for fast superset checking. It uses a variety of old and new pruning techniques to prune the search space. Experimental comparison with previous work reveals that FIMfi reduces the number of FP-trees created greatly and is more than 40% superior to the similar algorithms on average.Read the full paper here: https://link.springer.com/chapter/10.1007/978-3-540-30464-7_28
