Comparing the speed and time of association extraction from database with cuckoo search and genetic algorithms

International Journal of Smart Electrical Engineering

دوره 12، شماره 03، آذر 2023، صفحه 193-199   

نوع مقاله: Research Paper

شناسه دیجیتال (DOI): 10.30495/ijsee.2023.1974141.1240

نویسندگان

Payam Abdolmohammadi¹؛ Roham Farahani^{* 2}

¹Department of Computer Engineering, Islamic Azad University, Arak, Iran

²corresponding author

چکیده

This paper aims to study the appropriate data mining method to extract the rules from a data set and examining the benefits of using the cuckoo algorithm to extract association rules and compare the execution time of the cuckoo algorithm and genetic algorithm (GA). Therefore, an algorithm is proposed that includes two parts: preprocessing and mining. The first part presents the procedures related to the calculation of cuckoo fit values and in the second part of the algorithm, which is the main achievement of this research. Support and confidence The best position can show the least confidence and support.These mining results can be used to continue mining the association rules. The proposed algorithm is based on the cuckoo search. It hides the sensitive relationship rules with a lower time cost and, at the same time, controls the peripheral effects of non-sensitive rules in a better way. This aim is achieved using recurring to the objective function. The GA is set to be the evaluation criterion to show the prominence of the proposed method. In this method, we compare the speed of the cuckoo algorithm with the genetic algorithm, which uses genetic evolution as a problem-solving model. In general, it is an algorithm based on repetition, most of its parts are selected as random processes, and these algorithms are part of the fitting function. It was chosen as a criterion and we paid .It is scientifically proven that the cuckoo algorithm outperforms the GA in the execution time.

کلیدواژه‌ها

Association rules mining؛ genetic algorithm (GA)؛ cuckoo algorithm؛ sensitive relationship؛ non-sensitive relationship؛ data mining؛ association rules؛ dataset؛ time complexity؛ performance improvement