MWPCA-ICURD: density-based clustering method discovering specific shape original features
作者: Qinghua LuoYu PengJunbao LiXiyuan Peng
作者单位: 1Harbin Institute of Technology at WeiHai
2GuangXi Key Laboratory of Automatic Detecting Technology and Instruments (GuiLin University of Electronic Technology)
3State Key Laboratory of Geo-information Engineering
4State Key Laboratory of Satellite Navigation Engineering Technology
5Harbin Institute of Technology
刊名: Neural Computing and Applications, 2017, Vol.28 (9), pp.2545-2556
来源数据库: Springer Nature Journal
DOI: 10.1007/s00521-016-2208-9
关键词: Clustering for data with uncertaintiesWavelet transformPrincipal component analysisClustering using references and density
原始语种摘要: Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties, and it is called multivariate wavelet principal component analysis-improved clustering using references and density (MWPCA-ICURD), which utilizes feature extraction and density-based clustering. To cluster uncertain data with specific shape original features, the original features are extracted by MWPCA method, which combines digital wavelet decomposition and principal component analysis organically. Then, a density-based clustering...
全文获取路径: Springer Nature  (合作)
影响因子:1.168 (2012)

  • clustering 聚类
  • features 特征
  • shape 形状
  • density 密度
  • specific 
  • original 原图
  • uncertain 不确实的
  • component 成分
  • wavelet 波涟
  • organically 有机地