An irregular CLA-based novel frequent pattern mining approach

Frequent itemset mining has received a lot of attention in the field of data mining. Its main objective is to find groups of items that consistently appear together in datasets. Even while frequent itemset mining is useful, the algorithms for mining frequent itemsets have quite high resource requirements. In order to optimise the time and memory needs, a few improvements have been made in recent years. This study proposes CellFPM, a straightforward yet effective cellular learning automata-based method for finding frequent itemset occurrences. It works efficiently with large datasets. The efficiency of the proposed approach in time and memory requirements has been evaluated using benchmark datasets explicitly designed for performance measure. The varying size and density of the test datasets have confirmed the scalability of the suggested method. The findings show that CellFPM consistently surpasses the leading algorithms in terms of runtime and memory usage, particularly memory usage mostly.