基于类簇规模不均衡度量的粗糙模糊K-means聚类算法  被引量:9

Improved Rough Fuzzy K-means Clustering based on Imbalanced Measure of Cluster Sizes

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作  者:张腾飞[1] 李中文 马福民[2] 窦春霞[3] 彭晨[4] 岳东[1,3] ZHANG Tengfei;LI Zhongwen;MA Fumin;DOU Chunxia;PENG Chen;YUE Dong(College of Automation&College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;College of Information Engineering,Nanjing University of Finance and Economics,Nanjing 210023,China;Institute of Advanced Technology,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Mechatronics Engineering and Automation,Shanghai University,Shanghai 200072,China)

机构地区:[1]南京邮电大学自动化学院,人工智能学院,江苏南京210023 [2]南京财经大学信息工程学院,江苏南京210023 [3]南京邮电大学先进技术学院,江苏南京210023 [4]上海大学机电工程与自动化学院,上海200072

出  处:《信息与控制》2020年第3期281-288,共8页Information and Control

基  金:国家自然科学基金资助项目(61833011,61973151);江苏省自然科学基金资助项目(BK20191376,BK20191406);江苏省高校自然科学研究重大项目(17KJA120001);江苏省“六大人才高峰”高层次人才计划资助项目(XNY-038);南京邮电大学“1311人才计划”资助项目(NY2018)。

摘  要:粗糙模糊K-means (RFKM)聚类综合利用了粗糙集和模糊集的优势互补,是一种有效的聚类分析算法,但现有的RFKM算法大多只考虑了簇内样本空间分布的模糊度量,忽略了类簇规模的不均衡特征对聚类结果的影响,对类簇规模不均衡的数据集进行聚类分析时,适应性较差.为了能够从算法层面直接对类簇规模不均衡的数据集有效地进行聚类分析,引入了对类簇规模不均衡程度的自适应度量,并提出了一种基于类簇规模不均衡度量的粗糙模糊K-means聚类算法.通过人工数据集和UCI标准数据集验证了算法的有效性.Rough fuzzy K-means(RFKM)algorithm,which combines the advantages of rough sets and fuzzy sets,is an effective method to deal with boundary fuzzy data.Most of the existing RFKM and improved algorithms consider only the imbalanced space distribution of samples within the cluster while ignoring the impact of imbalanced cluster sizes on clustering results.Thus,these algorithms may have poor adaptability when faced with imbalanced datasets.To effectively address this problem at an algorithmic level,we introduce a measure of the degree of imbalanced cluster size.Thereafter,on the basis of the current RFKM algorithm,we develop an improved RFKM clustering based on imbalanced measure of cluster sizes.The validity of the algorithm is demonstrated through experimental analysis on the artificial dataset and UCI standard datasets.

关 键 词:粗糙模糊K-means聚类 粗糙集 模糊隶属度 类簇规模不均衡度量 

分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]

 

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