QUAC: Quick unsupervised anisotropic clustering
作者：
Highlights：
• Completely unsupervised clustering algorithm for multidimensional data.
• Anisotropic—it does not assume spherical clusters or use isotropic kernels.
• Fast—an excellent tool for performing rapid cluster analysis on data—much faster than mean-shift.
• Excellent initialisation for a Gaussian mixture model.
• Qualitative and quantitative results show superiority over well-known methods in accuracy and speed.
摘要
Highlights•Completely unsupervised clustering algorithm for multidimensional data.•Anisotropic—it does not assume spherical clusters or use isotropic kernels.•Fast—an excellent tool for performing rapid cluster analysis on data—much faster than mean-shift.•Excellent initialisation for a Gaussian mixture model.•Qualitative and quantitative results show superiority over well-known methods in accuracy and speed.
论文关键词：Clustering,Anisotropic,Gaussian Density
论文评审过程：Received 23 August 2012, Revised 23 April 2013, Accepted 2 May 2013, Available online 22 May 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.05.010
