Bit Splicing Frequent Itemset Mining Algorithm Based on Dynamic Grouping

Frequent itemset mining has always been one of the most classic tasks in data mining. It provides effective decision-making and judgment for many problems. A novel MPL (multi-partition list) structure is proposed in this paper combining bit combination and linear table structure. The MPL is composed of arrays where each unit stores a combination of items rather than a single item, which addresses the limitations of maintaining many pointers in the traditional tree structure. In addition, the MPL stores the least valid information required in the mining process. This paper further proposes a bit splicing frequent itemset mining algorithm based on dynamic grouping (BSFIM-DG) for the MPL. The algorithm dynamically calculates the number of grouping by using coverage according to the dataset’s characteristics. The candidate itemset is obtained by the bit-splicing method. The length of the MPL to be traversed is determined by the low-bit feature of the candidate itemset. The search space is reduced with the corresponding pruning strategy. Experiments on various open datasets demonstrate that the algorithm has excellent running speed, especially since the support is low. The proposed algorithm has a similar running speed to the BCLT-O and the FP-growth on some datasets. In terms of memory usage, the algorithm is better than the FP-growth and comparable to the BCLT-O, but there is still a particular gap with the Bit-combination algorithm. Nevertheless, as the pace of technology updates and iteration is getting faster and faster, it is very feasible to exchange space for speed.