华东理工大学学报（自然科学版）2017，Vol.43Issue(5)：669-676,8.DOI:10.14135/j.cnki.1006-3080.2017.05.011
基于谱聚类特征向量分析的模态划分方法
Mode Partitioning Method Based on Eigenvector Analysis in Spectral Clustering
摘要
Abstract
The multimode characteristics of the process data in actual production process will have a certain impact on the data modeling.Moreover,k-means,c-means and other clustering are several commonly used methods on mode analysis.However,these algorithms may not perform well in mode partitioning of the transition process.In this work,a general mode division method is proposed,in which the spectral clustering analysis of the similarity matrix is utilized.Moreover,by means of the relationship between the eigenvector of the similarity matrix and the involved classification information,a Gauss Manhattan distance is constructed for indicator variable such that the mode partitioning is achieved via the small window.Finally,the effectiveness of the proposed algorithm is verified by the experiment of multimode data with transition and nontransition process.关键词
多模态数据/模态划分/过渡过程/谱聚类Key words
multimode data/mode partitioning/transient process/spectral clustering分类
信息技术与安全科学引用本文复制引用
南男,杨健,赵晶晶,侍洪波..基于谱聚类特征向量分析的模态划分方法[J].华东理工大学学报（自然科学版）,2017,43(5):669-676,8.基金项目
国家自然科学基金(61374140,61673173) （61374140,61673173）
