信息与控制2018，Vol.47Issue(6)：722-729,8.DOI:10.13976/j.cnki.xk.2018.7478
簇特征加权的模糊紧致散布聚类算法
A Clustering Algorithm Based on Cluster Feature Weighting Fuzzy Compactness and Separation
摘要
Abstract
For the clustering of imbalanced data, we propose a clustering algorithm based on cluster feature weighting fuzzy compactness and separation (CFWFCS). By addressing the deficiency of the fuzzy membership formula in the fuzzy compactness and separation (FCS) algorithm, we provide formulations of sample membership and attribute weighting for every cluster of CFWFCS, and then discuss their adjustments. The proposed CFWFCS-based method is compared with the FCS algorithm and two other weighted-clustering algorithms on benchmark datasets. Experimental results show that the proposed algorithm outperforms the other three algorithms in accuracy and reasonability for unbalanced-distribution data.关键词
簇特征加权/模糊紧致/模糊散布/聚类/分布不均衡数据Key words
cluster feature weighting/fuzzy compactness/fuzzy separation/clustering/imblanced distribution data分类
信息技术与安全科学引用本文复制引用
周媛,束星玮,王蕾..簇特征加权的模糊紧致散布聚类算法[J].信息与控制,2018,47(6):722-729,8.基金项目
国家自然科学基金资助项目(61403198) （61403198）
江苏高校品牌专业建设工程资助项目(1181081501003) （1181081501003）
2017年国家电网总部科技计划资助项目 （）
南京信息工程大学大学生创新训练项目(1214071701214) （1214071701214）
