1 research outputs found
CorClustST - Correlation-based clustering of big spatio-temporal datasets
Increasing amounts of high-velocity spatio-temporal data reinforce the need for clustering algorithms
which are effective for big data processing and data reduction. As currently applied spatio-temporal
clustering algorithms have certain drawbacks regarding the comparability of the results, we propose an
alternative spatio-temporal clustering technique which is based on empirical spatial correlations over
time. As a key feature, CorClustST makes it easily possible to compare and interpret clustering results for
different scenarios such as multiple underlying variables or varying time frames. In a test case, we show
that the clustering strategy successfully identifies increasing spatial correlations of wind power forecast
errors in Europe for longer forecast horizons. An extension of the clustering algorithm is finally presented
which allows for a large-scale parallel implementation and helps to circumvent memory limitations. The
proposed method will especially be helpful for researchers who aim to preprocess big spatio-temporal
datasets and who intend to compare clustering results and spatial dependencies for different scenarios