Abstract

Big data mining is challenging.An effective algorithm and computer software are needed to solve problems while working with large datasets.The FP Growth Algorithm takes a long time to compute and extract results, and it demands a lot of memory.Right now, the FP_Growth algorithm is among the finest methods for mining frequent itemsets.The transaction dataset is used to create a tree structure, which is then recursively traversed to extract frequently occurring itemsets using a depth first search strategy.Additionally, creating an FP_tree requires time and suffers from growing larger FP_trees and producing a high number of frequent itemsets.In this paper, the suggest alterations to the FP_Growth algorithm's operation.With our usage of the proposed matrix OFIM to build a very compact FP-tree, the recommended approach would cut mining time and the number of regularly generated items, giving a considerable reduction in decision_making in large datasets.Furthermore, our technique significantly improves its speed in handling large datasets by limiting the amount of items that are produced often, thereby optimizing memory use.

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