eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based
on Bag-of-Features
Abstract
We propose eBoF, a novel time-varying ensemble data visualization
approach based on bag-of-features (BoF). In the eBoF model, we take a
simple and monotone interval from all target variables of ensemble
scalar data as a local feature patch of BoF model and the duration time
of each interval (i.e., feature patch) as its frequency. The feature
clusters in ensemble runs are then identified based on the similarity of
temporal correlations. eBoF generates the clusters together with their
probability distribution across all the feature patches while storing
the geo-spatial information, which is often lost in the traditional
topic modelling or clustering algorithms. The probability distribution
across different clusters can help to generate reasonable clustering
results evaluated by the domain knowledge. We conduct several case
studies and performance analyses. We also consult the domain experts to
evaluate the proposed eBoF model. Evaluation results suggest the
proposed eBoF can provide insightful and comprehensive evidence on
ensemble simulation data analysis.