A More Effective FP-Growth Algorithm for Big Data Using the FP_TDA Algorithm

The goal of association rule mining is to identify patterns in big data sets. Businesses may make well-informed decisions based on consumer behavior and preferences by using these links to uncover patterns or correlations that might not be immediately apparent. Apriori and FP-Growth are two examples of algorithms that companies may use to effectively extract insightful information from their data.The association rule method does, however, have certain limitations, including the requirement for a lot of memory, the necessity for extensive dataset searches to ascertain the item set's frequency, and sometimes less-than-ideal rules. The efficient algorithm Fp-TDA, based on the FP-Growth algorithm, would reduce the number of frequently formed items and the amount of time spent mining by using the proposed matrix TDA instead of the tree used in those methods. This would result in a significant reduction of the amount of decision-making in large datasets. By reducing redundancy, this method not only speeds up data processing but also increases the correctness of the output. As a result, the Fp-TDA algorithm has the potential to greatly enhance data mining applications, particularly in domains like market research and fraud detection where accuracy and speed are crucial.