[1]尹宏伟,杭雨晴,胡文军.融合异常检测与区域分割的高效K-means 聚类算法[J].郑州大学学报(工学版),2024,45(03):80-88.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 010]
 YIN Hongwei,HANG Yuqing,HU Wenjun.Efficient K-means with Region Segment and Outlier Detection[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):80-88.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 010]
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融合异常检测与区域分割的高效K-means 聚类算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年03期
页码:
80-88
栏目:
出版日期:
2024-04-20

文章信息/Info

Title:
Efficient K-means with Region Segment and Outlier Detection
文章编号:
1671-6833( 2024) 03-0080-09
作者:
尹宏伟12 杭雨晴12 胡文军12
1. 湖州师范学院 信息工程学院,浙江 湖州 313000;2. 湖州师范学院 浙江省现代农业资源智慧管理与应用研究重点实验室,浙江 湖州 313000
Author(s):
YIN Hongwei12 HANG Yuqing12 HU Wenjun12
1. College of Information Engineering, Huzhou University, Huzhou 313000, China; 2. Zhejiang Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China
关键词:
聚类 K-means 异常检测 区域分割 近邻簇搜索自适应
Keywords:
clustering K-means outlier detection region segment near neighbor clusters search adaption
分类号:
TP391
DOI:
10. 13705/ j. issn. 1671-6833. 2024. 03. 010
文献标志码:
A
摘要:
传统K-means 及其众多改进算法缺乏显式处理异常样本的能力,导致其聚类性能容易受到异常样本的影响。针对此问题,提出一种融合异常检测与区域分割的高效K-means 聚类算法。首先,通过构建统一聚类模型,形成异常检测与聚类之间的交互协同,以提高聚类性能。其次,利用近邻簇搜索技术对各类簇进行自适应的区域分割,以减少冗余计算,提高算法执行效率。最后,为验证所提方法的有效性,在多个合成数据集和真实数据集上分别进行测试。实验结果表明:所提算法聚类性能和执行效率优于其他算法;在添加10%异常样本的Wine 数据集上准确度可达0. 911。
Abstract:
In the traditional K-means and many improved algorithms, the inability to explicitly handle outliers, resulted in their poor clustering performance. To solve this problem, in this paper, an efficient K-means with region segment and outlier detection was proposed. Firstly, to obtain better clustering results, an unified clustering model to form an interactive collaboration between outlier detection and clustering was constructed. Secondly, to improve algorithm efficiency, clusters were adaptively segmented through near neighbor clusters search to reduce redundant calculations. Finally, on synthetic datasets and real datasets were tested to verify the effectiveness of the proposed method. The experimental results showed that EK-means algorithm outperformed other algorithms in terms of clustering performance and execution efficiency. The ACC could reach 0. 911 in the Wine dataset.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2023-10-20;修订日期:2023-11-24
基金项目:国家自然科学基金资助项目(62206094);湖州市公益性应用研究项目(2021GZ05);江苏省网络空间安全工程实验室开放课题(SDGC2237)
作者简介:尹宏伟(1990— ),男,安徽安庆人,湖州师范学院副教授,博士,主要从事机器学习、模式识别及数据挖掘等方面的研究,E-mail: 02713@ zjhu. edu. com。
通信作者:胡文军(1977— ),男,安徽宣城人,湖州师范学院教授,博士,主要从事机器学习、模式识别及智能系统等方面的研究, E-mail: hoowenjun@ foxmail. com。
更新日期/Last Update: 2024-04-29