Discovering the Rules of Data Mining Classification using Distributed Memetic Algorithm

Document Type : Original Article


1 azad univercity maybod,yazd,iran

2 2. Assistant Professor, Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran.


In distributed population systems, cohesive structures prevail, playing a crucial role in the evolution of species across different sites and fostering diversity. These structures employ local selection and reproduction methods to enhance the evolution process. Alterations in migration rules on certain sites, coupled with the execution of search operations, have led to a significant improvement in discovering classification rules. Ultimately, information sharing is employed to mitigate the complexity of the identified rule set. This study evaluates the effectiveness of the Distributed Memetic Algorithm in discovering classification rules in data mining. The algorithm is analyzed based on results obtained from five datasets collected from UCI and KEEL repositories. The findings indicate that the Distributed Memetic Algorithm outperforms the traditional Memetic Algorithm in precision for predicting and discovering classification rules in data mining. This research underscores the observable impact of migration operations and search execution in the process of discovering classification rules in data mining.


Main Subjects

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