Performance analysis of frequent pattern mining algorithm on different real-life dataset

The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four reallife datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.