基于混合鲸优化算法的关联规则挖掘

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Z. Ye, Wenhui Cai, Mingwei Wang, Aixin Zhang, Wen-hua Zhou, Na Deng, Zimei Wei, Daxin Zhu
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引用次数: 1

摘要

关联规则挖掘(ARM)是数据挖掘中最重要和最活跃的研究领域之一。近年来,鲸鱼优化算法(Whale Optimization Algorithm, WOA)在数据挖掘领域得到了成功的应用,但该算法容易陷入局部最优。为此,将改进的基于WOA的自适应参数策略和Levy Flight机制(LWOA)应用于关联规则挖掘。同时,提出了一种混合两种算法来平衡探索和开发阶段的混合策略,即灰狼优化算法(GWO)、人工蜂群算法(ABC)和布谷鸟搜索算法(CS)致力于提高LWOA的收敛性。该方法通过将规则挖掘任务建模为同时满足支持度、置信度、提升度和特定因子的多目标问题,并在多个数据集上进行检查,从而进行全局搜索并找到关联规则集。实验结果表明,与其他算法相比,该方法具有更好的挖掘性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association Rule Mining Based on Hybrid Whale Optimization Algorithm
Association Rule Mining(ARM) is one of the most significant and active research areas in data mining. Recently, Whale Optimization Algorithm (WOA) has been successfully applied in the field of data mining, however, it easily falls into the local optimum. Therefore, an improved WOA based adaptive parameter strategy and Levy Flight mechanism (LWOA) is applied to mine association rules. Meanwhile, a hybrid strategy that blends two algorithms to balance the exploration and exploitation phases is put forward, that is, grey wolf optimization algorithm (GWO), artificial bee colony algorithm (ABC) and cuckoo search algorithm (CS) are devoted to improving the convergence of LWOA. The approach performs a global search and finds the association rules sets by modeling the rule mining task as a multi-objective problem that simultaneously meets support, confidence, lift, and certain factor, which is examined on multiple data sets. Experimental results verify that the proposed method has better mining performance compared to other algorithms involved in the paper.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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