Frequent Itemset Mining Algorithm Based on Linear Table

Abstract

Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number of scans to the original dataset. Furthermore, operations such as pruning and grouping are also used to optimize the algorithm. For different datasets, the algorithm shows different mining speeds. (1) In sparse datasets, the algorithm achieves an average 45% improvement in mining speed over the bit combination algorithm, and a 2-3 times improvement for the classic FP-growth algorithm. (2) In dense datasets, the average improvement over the classic FP-growth algorithm is 50-70%. For the bit combination algorithm, there are dozens of times of improvement. In fact, the algorithm that integrates bit combinations with bitwise AND operation can effectively avoid recursive operations and it is beneficial to the parallelization. Further analysis shows that the linear table is easy to split to facilitate the data batch mining processing.

Innovations in frequent itemset mining: challenges and opportunities

“…The significance of frequent itemset mining lies in its capacity to unearth associations and patterns within the data [11,12]. These patterns are essential for making data-driven decisions, identifying trends, and gaining deeper insights into complex datasets [13,14]. Consequently, researchers and practitioners have harnessed this technique to extract valuable information and drive data-driven processes in numerous industries and applications [15,16].…”