Selecting Time Series Clustering Methods based on Run-Time Costs
Sprache des Vortragstitels:
Englisch
Original Kurzfassung:
Clustering time series, e.g., of monitoring data from
software systems, can reveal important insights and
interesting hidden patterns. However, choosing the
right method is not always straightforward, especially
as not only clustering quality but also run-time costs
must be considered. In this paper, we thus present an
approach that aids users in selecting the best methods
in terms of quality as well as computational costs.
Given a set of candidate methods, we evaluate their
clustering performance and robustly measure their actual
run times, i.e., the execution time on a specific
machine. We evaluate our approach using data from
the UCR time series archive and show its usefulness
in determining the best clustering methods while also
taking costs into account.
Forschungs-