Association Rule Mining Based on Bat Algorithm

摘要

Data mining is the process of extracting useful knowledge from a large database by using software and tools to look for discrimination and expressive patterns. This process helps companies to focus on important information in their historical databases to make decisions. Association rule mining is one of the most important domain in data mining. It aims to extract correlations, frequent pattern and associations between the items in databases 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 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.