The cold start problem continues to pose a significant challenge in collaborative filtering-based recommender systems, particularly when user data is sparse or newly introduced. This study presents a comparative analysis between traditional frequent pattern mining (FPM) approaches and the Fast Incremental Updated Frequent Pattern mining algorithm (FIUFP) to improve both the performance and adaptability of recommendation systems. The proposed methodology leverages Principal Component Analysis (PCA) for dimensionality reduction and K-Means clustering to group users based on demographic features. Within each cluster, frequent pattern mining is employed to extract relevant association rules. Experiments conducted on the MovieLens 100K dataset demonstrate that the FIUFP algorithm outperforms conventional FPM methods by significantly reducing execution time (1.2 s vs. 5.9 s), lowering memory usage, and generating a higher number of strong rules. FIUFP achieved an accuracy of 78.2% and an F1 score of 0.411. This indicates that it is effective when incrementally updating data and addressing cold-start issues. Overall, these outcomes demonstrate that FIUFP is a realistic and scalable option for modern data-driven recommender systems.