A benchmark study on time series clustering. Javed, A., Lee, B. S., & Rizzo, D. M. Machine Learning with Applications, 1:100001, September, 2020. ![A benchmark study on time series clustering [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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, dynamic time warping, and shape-based), while adhering to six restrictions on datasets and methods to make the comparison as unbiased as possible. A phased evaluation approach was then designed for summarizing dataset-level assessment metrics and discussing the results. The benchmark study presented can be a useful reference for the research community on its own; and the dataset-level assessment metrics reported may be used for designing evaluation frameworks to answer different research questions.
@article{javed_benchmark_2020,
title = {A benchmark study on time series clustering},
volume = {1},
issn = {2666-8270},
url = {http://www.sciencedirect.com/science/article/pii/S2666827020300013},
doi = {10.1016/j.mlwa.2020.100001},
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, dynamic time warping, and shape-based), while adhering to six restrictions on datasets and methods to make the comparison as unbiased as possible. A phased evaluation approach was then designed for summarizing dataset-level assessment metrics and discussing the results. The benchmark study presented can be a useful reference for the research community on its own; and the dataset-level assessment metrics reported may be used for designing evaluation frameworks to answer different research questions.},
language = {en},
urldate = {2020-10-05},
journal = {Machine Learning with Applications},
author = {Javed, Ali and Lee, Byung Suk and Rizzo, Donna M.},
month = sep,
year = {2020},
keywords = {Benchmark, Clustering, Time series, UCR archive},
pages = {100001},
}
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