Original paper
A benchmark study on time series clustering
Abstract
This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive — the state of the art repository of time series data. Specifically, the benchmark examines eight popular clustering methods representing three categories of clustering algorithms (partitional, hierarchical and density-based) and three types of distance measures (Euclidean,...
Paper Details
Title
A benchmark study on time series clustering
Published Date
Aug 3, 2020
Volume
1
Pages
100001 - 100001