A Maximal Frequent Itemset Mining Algorithm Based on Index Array and Set-Enumeration Tree
基于索引数组与集合枚举树的最大频繁项集挖掘算法

Because of the inherent computational complexity, mining the complete frequent itemset in dense datasets remains to be a challenging task. Mining Maximal Frequent Itemset (MFIis an alternative to address the problem. Set-Enumeration Tree (SETis a common data structure used in several MFI mining algorithms. For this kind of algorithms, the process of mining MFI can also be viewed as the process of searching in SET. To reduce the search space of SET, in this paper, a new algorithm, Index-MaxMiner, for mining MFI is proposed by employing a hybrid search strategy blending breadth-first and depth-first combined. Firstly, the "index array" is proposed, and by using bitmap, an algorithm for computing index array is presented. By adding subsume index to frequent items, Index-MaxMiner discovers the candidate MFIs using breadth-first search at one time. By doing so, the number of nodes in the first level of SET is reduced greatly. Then, for candidate MFIs, depth-first search strategy is used to generate all MFIs. Thus, the jumping search in SET is implemented, and the search space is reduced greatly. The experimental results show that the proposed algorithm outperforms similar state-of-the-art algorithms.