Association Rule Mining Based on Bat Algorithm
Kamel Eddine Heraguemi, Nadjet Kamel, Habiba Drias · Journal of Computational and Theoretical Nanoscience · 2015
In this paper, we propose a bat-based algorithm (BA) for association rule mining (ARM Bat). Our algorithm aims to maximize the fitness function to generate the best rules in the defined dataset starting from a specific minimum support and minimum confidence. The efficiency of our proposed algorithm is tested on several generic datasets with different number of transactions and items. The results are compared to FPgrowth algorithm results on the same datasets. ARM bat algorithm perform better than the FPgrowth algorithm in term of computation speed and memory usage,