Comparative Analysis of Performance in FP-Growth and Apriori Algorithm

This research paper presents a comparative analysis of two popular data mining algorithms, Apriori and FP Growth, using the WEKA tool. The study aims to determine which algorithm is more efficient in terms of execution time and database scan parameters. The results indicate that FP Growth outperforms Apriori in terms of execution time and number of database scans required to generate frequent item sets. The study also examines the impact of various parameters on the performance of both algorithms, such as support threshold and database size. The findings of this study suggest that FP Growth is a more effective algorithm for frequent itemset mining, especially for larger databases, and can be a useful tool for data analysts and researchers working in this field.