{"title":"A Binary Grey Wolf Optimizer with Mutation for Mining Association Rules","authors":"K. Heraguemi, Nadjet Kamel, Majdi M. Mafarja","doi":"10.56415/csjm.v32.06","DOIUrl":null,"url":null,"abstract":"In this decade, the internet becomes indispensable in companies and people life. Therefore, a huge quantity of data, which can be a source of hidden information such as association rules that help in decision-making, is stored. Association rule mining (ARM) becomes an attractive data mining task to mine hidden correlations between items in sizeable databases. However, this task is a combinatorial hard problem and, in many cases, the classical algorithms generate extremely large number of rules, that are useless and hard to be validated by the final user. In this paper, we proposed a binary version of grey wolf optimizer that is based on sigmoid function and mutation technique to deal with ARM issue, called BGWOARM. It aims to generate a minimal number of useful and reduced number of rules. It is noted from the several carried out experimentations on well-known benchmarks in the field of ARM, that results are promising, and the proposed approach outperforms other nature-inspired algorithms in terms of quality, number of rules, and runtime consumption.","PeriodicalId":42293,"journal":{"name":"Computer Science Journal of Moldova","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Journal of Moldova","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56415/csjm.v32.06","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
In this decade, the internet becomes indispensable in companies and people life. Therefore, a huge quantity of data, which can be a source of hidden information such as association rules that help in decision-making, is stored. Association rule mining (ARM) becomes an attractive data mining task to mine hidden correlations between items in sizeable databases. However, this task is a combinatorial hard problem and, in many cases, the classical algorithms generate extremely large number of rules, that are useless and hard to be validated by the final user. In this paper, we proposed a binary version of grey wolf optimizer that is based on sigmoid function and mutation technique to deal with ARM issue, called BGWOARM. It aims to generate a minimal number of useful and reduced number of rules. It is noted from the several carried out experimentations on well-known benchmarks in the field of ARM, that results are promising, and the proposed approach outperforms other nature-inspired algorithms in terms of quality, number of rules, and runtime consumption.
在这十年里,互联网已成为公司和人们生活中不可或缺的一部分。因此,大量数据被存储起来,这些数据可能是有助于决策的关联规则等隐藏信息的来源。关联规则挖掘(ARM)成为一项极具吸引力的数据挖掘任务,它可以在庞大的数据库中挖掘项目之间隐藏的关联。然而,这项任务是一个难以解决的组合问题,在很多情况下,经典算法会生成大量规则,而这些规则毫无用处,也很难被最终用户验证。在本文中,我们提出了一种基于西格玛函数和突变技术的二进制灰狼优化器来解决 ARM 问题,称为 BGWOARM。它旨在生成最少的有用规则,并减少规则数量。在 ARM 领域的知名基准上进行的几项实验表明,结果很有希望,所提出的方法在质量、规则数量和运行时间消耗方面都优于其他自然启发算法。