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

作　　者：李晓庆 唐昊[1] 司加胜 苗刚中[1] LI Xiao-Qing;TANG Hao;SI Jia-Sheng;MIAO Gang-Zhong(School of Electrical Engineering and Automation,Hefei University of Technology,Hefei 230009)

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009

出　　处：《自动化学报》2018年第12期2259-2268,共10页Acta Automatica Sinica

基　　金：国家重点研发计划(2017YFB0902600);国家自然科学基金(61573126)资助~~

摘　　要：针对混合属性数据集聚类精度低的问题,本文提出一种基于改进距离度量的半监督模糊均值聚类(Fuzzy C-means,FCM)算法.首先,在数据集中针对类别属性进行预处理,并设置相应的相异度阈值;将传统聚类距离度量与改进的Jaccard距离度量结合,确定混合属性数据集的距离度量函数;最后,将所得距离度量函数与传统半监督FCM算法相结合,并在滚动轴承的不同复合故障数据的特征集中进行聚类.实验表明,该算法能在含无序属性的混合属性数据集的聚类中取得更好的聚类效果.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 Jaccard 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]