[1]何菲菲,韩萌,张瑞华,等.象群优化的高效用项集挖掘算法[J].南京师大学报(自然科学版),2025,48(02):124-138.[doi:10.3969/j.issn.1001-4616.2025.02.013]
　He Feifei,Han Meng,Zhang Ruihua,et al.Elephant Herding Optimization Algorithm for Mining High Utility Itemsets[J].Journal of Nanjing Normal University(Natural Science Edition),2025,48(02):124-138.[doi:10.3969/j.issn.1001-4616.2025.02.013]
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象群优化的高效用项集挖掘算法()

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
48
期数:
2025年02期
页码:
124-138
栏目:
计算机科学与技术
出版日期:
2025-04-15

文章信息/Info

Title:
Elephant Herding Optimization Algorithm for Mining High Utility Itemsets
文章编号:
1001-4616(2025)02-0124-15
作者:
何菲菲; 韩萌; 张瑞华; 李春鹏; 孟凡兴
(北方民族大学计算机科学与工程学院,宁夏 银川 750021)
Author(s):
He Feifei; Han Meng; Zhang Ruihua; Li Chunpeng; Meng Fanxing
(School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China)
关键词:
高效用项集挖掘; 启发式算法; 象群优化; 进化策略; 多样性维护策略
Keywords:
high utility itemset mining; heuristic algorithms; elephant herding optimization; evolution strategy; population diversity maintenance strategy
分类号:
TP311.13
DOI:
10.3969/j.issn.1001-4616.2025.02.013
文献标志码:
A
摘要:
启发式高效用项集挖掘是近年数据挖掘领域的一个热点研究课题. 为了解决启发式高效用项集挖掘算法过早收敛导致的项集丢失问题,设计了一种新的启发式高效用项集挖掘算法,旨在较少的迭代次数内获取更多的高效用项集. 其中,提出的基于母象因子的位差进化策略,有效缩减了搜索空间,提高了算法的执行效率. 为了防止算法收敛过快陷入局部最优,提出两阶段种群多样性维护策略,保持了种群多样性和收敛性间的平衡. 在真实数据集上进行的大量实验表明,提出的算法在高效用项集数量、时空效率和算法收敛性方面均优于现有的先进算法.
Abstract:
Heuristic high utility itemset mining is an active research topic in the field of data mining in recent years. To solve the problem of itemset loss caused by the early convergence of heuristic high utility itemset mining algorithms,a new algorithm is designed to discovering more high utility itemsets within fewer iterations. The proposed strategy of positional evolution based on the female elephant factor is proposed to reduce effectively the search space and improve the execution efficiency of the algorithm. Moreover,in order to prevent the algorithm from converging too quickly and falling into local optimum,the proposed strategy of two-phase population diversity maintenance which keeps a balance between population diversity and convergence. Extensive experiments on real datasets show that the proposed algorithm outperforms the advanced heuristic high utility mining algorithms.

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备注/Memo

备注/Memo:
收稿日期:2024-07-01.
基金项目:国家自然科学基金项目(62062004)、宁夏自然科学基金项目(2023AAC03315)、北方民族大学中央高校基本科研业务费专项资金资助项目(2021KJCX10)、北方民族大学研究生创新项目(YCX24124).
通讯作者:韩萌,博士,教授,研究方向:大数据挖掘. E-mail:2003051@nmu.edu.cn
更新日期/Last Update: 2025-04-15