Persistent homology for time series and spatial data clustering
作者：
Highlights：
• We explore computational topology for clustering.
• We focus on time series and spatial data.
• Shape features are extracted based on n-dimensional holes.
• We run experiments with synthetic and real-world data.
• Results show improvements when compared to traditional clustering.
摘要
•We explore computational topology for clustering.•We focus on time series and spatial data.•Shape features are extracted based on n-dimensional holes.•We run experiments with synthetic and real-world data.•Results show improvements when compared to traditional clustering.
论文关键词：Data clustering,Spatial data,Time series,Persistent homology,Topological data analysis
论文评审过程：Available online 16 April 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.010
