Developing an Efficient FP-Growth Algorithm using Ordered Frequent Itemsets Matrix for Big Data
Keywords:
Big Data, Aprioiri Algorithm, FP-Growth Algorithm, Support CountAbstract
Mining big data is difficult. Problems require an efficient algorithm and software computer for computation in big datasets The FP Growth Algorithm needs a lot of memory and requires a long time for computation and extract result. In this work, we propose modifications to the workings of the FP-Growth algorithm. The suggested algorithm will reduce the time in mining and decrease the number of frequently created items, yielding a significant reduction in decision-making in big datasets through our use of the proposed matrix OFIM instead of the tree used in those algorithms. The matrix OFIM allows for efficient storage and retrieval of frequent itemsets, resulting in faster computation and extraction of results compared to the traditional tree-based approach. Additionally, our algorithm optimizes memory usage by minimizing the number of frequently created items, further enhancing its performance in handling big datasets.
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