邱云飞2,
刘万军2,
刘大千3
1.辽宁工程技术大学工商管理学院 ??葫芦岛 ??125105
2.辽宁工程技术大学软件学院 ??葫芦岛 ??125105
3.辽宁工程技术大学电子与信息工程学院 ??葫芦岛 ??125105
基金项目:国家自然科学基金青年科学基金(61401185)
详细信息
作者简介:费博雯:女,1991年生,博士生,研究方向为数据挖掘与智能数据处理
邱云飞:男,1976年生,博士,教授,主要研究方向为数据挖掘与智能数据处理
刘万军:男,1959年生,硕士,教授,主要研究方向为图像与视觉信息计算、运动目标检测与跟踪
刘大千:男,1992年生,博士生,研究方向为图像与视觉信息计算、运动目标检测与跟踪
通讯作者:费博雯 feibowen2098@163.com
中图分类号:TP391计量
文章访问数:1665
HTML全文浏览量:453
PDF下载量:35
被引次数:0
出版历程
收稿日期:2017-11-15
修回日期:2018-05-09
网络出版日期:2018-06-07
刊出日期:2018-08-01
Fuzzy Clustering Ensemble Model Based on Distance Decision
Bowen FEI1,,,Yunfei QIU2,
Wanjun LIU2,
Daqian LIU3
1. School of Business Administration, Liaoning Technical University, Huludao 125105, China
2. School of Software, Liaoning Technical University, Huludao 125105, China
3. School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China
Funds:The Young Scientists Fund of the National Natural Science Foundation of China (61401185)
摘要
摘要:模糊聚类是近年来使用的一类性能较为优越的聚类算法,但该类算法对初始聚类中心敏感且对边界样本的聚类结果不够准确。为了提高聚类准确性、稳定性,该文通过联合多个模糊聚类结果,提出一种距离决策下的模糊聚类集成模型。首先,利用模糊C均值(FCM)算法对数据样本进行多次聚类,得到相应的隶属度矩阵。然后,提出一种新的距离决策方法,充分利用得到的隶属度关系构建一个累积距离矩阵。最后,将距离矩阵引入密度峰值(DP)算法中,利用改进的DP算法进行聚类集成以获取最终聚类结果。在UCI机器学习库中选择9个数据集进行测试,实验结果表明,相比经典的聚类集成模型,该文提出的聚类集成模型效果更佳。
关键词:模糊聚类/
集成模型/
距离决策/
隶属度矩阵/
密度峰值算法
Abstract:Fuzzy clustering is a kind of clustering algorithm which shows superior performance in recent years, however, the algorithm is sensitive to the initial cluster center and can not obtain accurate results of clustering for the boundary samples. In order to improve the accuracy and stability of clustering, this paper proposes a novel approach of fuzzy clustering ensemble model based on distance decision by combining multiple fuzzy clustering results. First of all, performing several times clustering for data samples by using FCM (Fuzzy C-Means), and corresponding membership matrices are obtained. Then, a new method of distance decision is proposed, a cumulative distance matrix is constructed by the membership matrices. Finally, the distance matrix is introduced into the Density Peaks (DP) algorithm, and the final results of clustering are obtained by using the improved DP algorithm for clustering ensemble. The results of the experiment show that the clustering ensemble model proposed in this paper is more effective than other classical clustering ensemble model on the 9 data sets in UCI machine learning database.
Key words:Fuzzy clustering/
Ensemble model/
Distance decision/
Membership matrices/
Density Peaks (DP) algorithm
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