Market Basket Analysis (MBA) identifies co-occurring products for pricing, cross-selling, and placement. This paper reviews Apriori and FP-Growth and introduces the RN-Algorithm, a method that focuses on rules with high lift and confidence by measuring pairwise item co-occurrence. In retail-style transactions, RN provides competitive or superior performance in identifying the top rules while remaining simple to distribute. This study examines RN’s complexity, how its runtime scales with the number of unique items and basket width, and offers a practitioner-oriented comparison of algorithms. We also address RN’s limitations (e.g., pair growth in wide baskets) and propose mitigations, including minimum item support, top-K selection, and distributed counting. The paper concludes with actionable guidance on when to prefer RN over FP-Growth or Apriori in modern e-commerce applications.