Modified Improved Apriori Algorithm for Reduced Time Complexity

In today's era, transactional datasets are ubiquitous and continue to grow in size, making it increasingly challenging to mine frequent itemsets efficiently. The Apriori algorithm is a well-known method for mining frequent itemsets, but it suffers from high time complexity as the size of the dataset increases. In this paper, we propose a multicore modified Apriori algorithm using machine learning to address this challenge. Our proposed algorithm leverages the parallel processing capability of modern multicore processors and integrates machine learning techniques to reduce the time complexity of the Apriori algorithm. The results of our experiments show that our proposed algorithm significantly outperforms other Apriori algorithms in terms of runtime, making it a promising solution for mining frequent itemsets from large transactional datasets.