[1]单芝慧,韩 萌,韩 强.基于滑动窗口的数据流高效用模糊项集挖掘[J].南京师大学报(自然科学版),2023,46(01):120-129.[doi:10.3969/j.issn.1001-4616.2023.01.016]
　Shan Zhihui,Han Meng,Han Qiang.High Utility Fuzzy Itemsets Mining Over Data Stream Based on Sliding Window Model[J].Journal of Nanjing Normal University(Natural Science Edition),2023,46(01):120-129.[doi:10.3969/j.issn.1001-4616.2023.01.016]
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基于滑动窗口的数据流高效用模糊项集挖掘()
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《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

卷:
第46卷
期数:
2023年01期
页码:
120-129
栏目:
计算机科学与技术
出版日期:
2023-03-15

文章信息/Info

Title:
High Utility Fuzzy Itemsets Mining Over Data Stream Based on Sliding Window Model
文章编号:
1001-4616(2023)01-0120-10
作者:
单芝慧; 韩 萌; 韩 强
(1.北方民族大学计算机科学与工程学院，宁夏 银川 750021)
(2.北方民族大学图像图形智能处理国家民委重点实验室，宁夏 银川 750021)
Author(s):
Shan Zhihui; Han Meng; Han Qiang
(1.School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China)
(2.The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China)
关键词:
数据流挖掘; 滑动窗口; 高效用项集挖掘; 模糊效用; 效用列表
Keywords:
data stream mining;  sliding window;  high utility itemsets mining;  fuzzy utility;  utility list
分类号:
TP391
DOI:
10.3969/j.issn.1001-4616.2023.01.016
文献标志码:
A
摘要:
高效用项集挖掘可以提供有趣的结果集,但并不能提供单个项的数量,因此,本文提出了高效用模糊项集. 但是,现实世界的数据是不断出现的,需要实时处理新到来的数据. 为解决当前高效用模糊项集不能处理数据流的问题,又提出了模糊效用列表(fuzzy utility list,FUL)结构用于存储当前窗口中项的批次号、项在事务中的事务标识符、项的模糊效用以及项的剩余模糊效用,该结构能有效的对批次进行插入和删除操作. 最后,基于FUL提出了数据流高效用模糊项集挖掘算法. 对真实数据集和合成数据集进行了广泛的实验,结果证实了算法的效率及可行性.
Abstract:
High-utility itemsets mining(HUI)can provide interesting itemsets,but cannot provide information on the number of items. Therefore,high utility fuzzy itemsets are proposed. However,real-world data is constantly emerging. Thus,new incoming data needs to be processed in real time. To solve the problem that the current high utility fuzzy itemsets cannot handle the data stream,a fuzzy utility list(FUL)structure is proposed to store the information of items,including batch number of items,the transaction identifier of the items,the fuzzy utility of items,and the reminding fuzzy utility of items. FUL can effectively insert and delete batches. Finally,based on FUL,a high utility fuzzy itemset mining algorithm on data stream is proposed,extensive experiments on real and synthetic datasets show the efficiency and feasibility of the algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2022-08-08.
基金项目:国家自然科学基金项目(62062004、61862001)、宁夏自然科学基金项目(2020AAC03216).
通讯作者:韩萌,博士,教授,研究方向:数据挖掘. E-mail:2003051@nmu.edu.cn
更新日期/Last Update: 2023-03-15