This document proposes a hybrid recommendation framework that uses both clustering and association rule mining. It first clusters users using the k-means algorithm to group similar users together. It then applies the Eclat algorithm to generate frequent itemsets and association rules from the user-item data. These association rules are used to make recommendations to users. The framework aims to address issues like sparsity and cold start problems. An experimental study compares the performance of the Eclat and Apriori algorithms on recommendation quality and execution time. The proposed approach integrates clustering and association rule mining to provide more accurate recommendations.