自动化学报2018，Vol.44Issue(12)：2259-2268,10.DOI:10.16383/j.aas.2018.c170510
面向混合属性数据集的改进半监督FCM聚类方法
An Improved Semi-supervised FCM Clustering Method for Mixed Data Sets
摘要
Abstract
This paper puts forward a semi-supervised fuzzy C-means (FCM) algorithm based on an improved distance measure to solve the problem of low accuracy of clustering algorithm of data sets with mixed attributes. First, the classification attributes are preprocessed in the data set, and the corresponding dissimilarity threshold is set. Then the traditional clustering distance measure is combined with the improved Jaccard distance measure to determine the distance measure function. Finally, the distance measure function is combined with the traditional semi-supervised FCM algorithm, and clustering is carried out on the characteristic data sets of different coupling fault data of rolling bearings. Simulation results show that the algorithm can achieve better clustering accuracy in mixed data sets.关键词
混合属性/相异度阈值/模糊均值聚类/JaccardKey words
Mixed attributes/dissimilarity threshold/fuzzy C-means (FCM) /Jaccard引用本文复制引用
李晓庆,唐昊,司加胜,苗刚中..面向混合属性数据集的改进半监督FCM聚类方法[J].自动化学报,2018,44(12):2259-2268,10.基金项目
国家重点研发计划(2017YFB0902600) （2017YFB0902600）
国家自然科学基金(61573126)资助 （61573126）
