Authors : Abdulkader Mohammed Abdulla Al-Badani

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/4kxxekfa

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DOI : https://doi.org/10.38124/ijisrt/25dec579

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Abstract : To efficiently analyze huge datasets, mining big data requires advanced computational techniques and algorithms. Apriori and FP-Growth are two of the most well-known algorithms in data mining. They help businesses make decisions based on customer trends and behaviors by finding patterns and correlations. Machine learning has made these algorithms even better by making them more accurate and efficient. The association rule approach does have some problems, though. For example, it needs a lot of memory, it has to search through all the data sets to find the frequency of an item set, and it sometimes makes rules that aren't the best. This study conducts a comparative analysis of the FP-Growth, Apriori, and TDA algorithms, demonstrating notable performance differences. The FP-Growth algorithm was much better at working with large datasets than the Apriori method, which had problems with scalability and took longer to process larger datasets, even though it was easier to build. This study suggests changes to the FP-Growth algorithm to fix these problems. It uses the TDA matrix to make a very compact FP-tree. This method tries to cut down on the time it takes to mine and the number of items that are created, which will make memory use more efficient and speed up processing for large datasets. In short, the proposed method is a promising way to make data mining processes more efficient and scalable, especially when it comes to big data analytics.

Keywords : FP-Growth Algorithm, Aprioiri Algorithm, FP-tree, Support Count, TDA.

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