The paper presents an efficient algorithm for mining association rules between sets of items in large databases of customer transactions. Each transaction consists of items purchased by a customer, and the goal is to find rules that have a minimum transactional support and confidence. The algorithm decomposes the problem into two subproblems: finding large itemsets and generating rules from these itemsets. It uses estimation techniques to determine which itemsets to measure in each pass, pruning techniques to avoid measuring certain itemsets, and buffer management to handle memory constraints. The effectiveness of the algorithm is demonstrated through experiments on sales data from a large retail company, showing high accuracy in estimation and significant pruning of itemsets. The paper also discusses related work in rule discovery and highlights the practical applications of the proposed algorithm in business decision-making.The paper presents an efficient algorithm for mining association rules between sets of items in large databases of customer transactions. Each transaction consists of items purchased by a customer, and the goal is to find rules that have a minimum transactional support and confidence. The algorithm decomposes the problem into two subproblems: finding large itemsets and generating rules from these itemsets. It uses estimation techniques to determine which itemsets to measure in each pass, pruning techniques to avoid measuring certain itemsets, and buffer management to handle memory constraints. The effectiveness of the algorithm is demonstrated through experiments on sales data from a large retail company, showing high accuracy in estimation and significant pruning of itemsets. The paper also discusses related work in rule discovery and highlights the practical applications of the proposed algorithm in business decision-making.