1 research outputs found
A Benchmark Study on Time Series Clustering
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). We lay out six restrictions
with special attention to making the benchmark 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.Comment: Typos corrected, figures resolution change